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  • 1.
    Deb, Mainak
    et al.
    Wipro Technol, India.
    Dhal, Krishna Gopal
    Midnapore Coll Autonomous, India.
    Das, Arunita
    Midnapore Coll Autonomous, India.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Middle East Univ, Jordan; Appl Sci Private Univ, Jordan.
    Abualigah, Laith
    Al Ahliyya Amman Univ, Jordan; Al al Bayt Univ, Jordan; Univ Tabuk, Saudi Arabia; Lebanese Amer Univ, Lebanon; Yuan Ze Univ, Taiwan.
    Garai, Arpan
    Indian Inst Technol, India.
    A CNN-based model to count the leaves of rosette plants (LC-Net)2024Ingår i: Scientific Reports, E-ISSN 2045-2322, Vol. 14, nr 1, artikel-id 1496Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Plant image analysis is a significant tool for plant phenotyping. Image analysis has been used to assess plant trails, forecast plant growth, and offer geographical information about images. The area segmentation and counting of the leaf is a major component of plant phenotyping, which can be used to measure the growth of the plant. Therefore, this paper developed a convolutional neural network-based leaf counting model called LC-Net. The original plant image and segmented leaf parts are fed as input because the segmented leaf part provides additional information to the proposed LC-Net. The well-known SegNet model has been utilised to obtain segmented leaf parts because it outperforms four other popular Convolutional Neural Network (CNN) models, namely DeepLab V3+, Fast FCN with Pyramid Scene Parsing (PSP), U-Net, and Refine Net. The proposed LC-Net is compared to the other recent CNN-based leaf counting models over the combined Computer Vision Problems in Plant Phenotyping (CVPPP) and KOMATSUNA datasets. The subjective and numerical evaluations of the experimental results demonstrate the superiority of the LC-Net to other tested models.

  • 2.
    Ebeed, Mohamed
    et al.
    Sohag Univ, Egypt; Univ Jaen, Spain.
    Abdelmotaleb, Mohamed A.
    Sohag Univ, Egypt.
    Khan, Noor Habib
    North China Elect Power Univ, Peoples R China.
    Jamal, Raheela
    North China Elect Power Univ, Peoples R China.
    Kamel, Salah
    Aswan Univ, Egypt.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Middle East Univ, Jordan.
    Zawbaa, Hossam M.
    Beni Suef Univ, Egypt; Appl Sci Private Univ, Jordan.
    Jurado, Francisco
    Univ Jaen, Spain.
    Sayed, Khairy
    Sohag Univ, Egypt.
    A Modified Artificial Hummingbird Algorithm for solving optimal power flow problem in power systems2024Ingår i: Energy Reports, E-ISSN 2352-4847, Vol. 11, s. 982-1005Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Optimal power flow (OPF) problem solution is a crucial task for the operators and decision makers to assign the best setting of the system components to obtain the most economic, environmental, and technical suitable state. Artificial Hummingbird Algorithm is a recent optimization algorithm that has been applied to solving several optimization problems. In this paper, a Modified Artificial Hummingbird Algorithm (MAHA) is proposed for improving the performance of the orignal Artificial Hummingbird Algorithm as well as effectivelly solve the OPF problem. The proposed MAHA is based on improving the searching capability by boosting the exploitation using the bandwidth motion around the best solution, while the exploration process is improved using the Levy flight distribution motion and the fitness-distance balance selection. This modified version helps overcome issues such as stagnation, premature convergence, and a propensity for local optima when tackling complex, nonlinear, and non-convex optimization problems like OPF. In order to confirm the effectiveness of the proposed algorithm, a series of tests are conducted on 23 standard benchmark functions, including CEC2020. The resulting outcomes are then compared to those obtained using other algorithms such as fitness-distance balance selection-based stochastic fractal search (FDBSFS), antlion optimizer (ALO), whale optimization algorithm (WOA), sine-cosine algorithm (SCA), fitness-distance balance and learning based artificial bee colony (FDB-TLABC), and traditional artificial hummingbird algorithm (AHA).The proposed algorithm is evaluated by solving the OPF problem with multiple objective functions on the IEEE 30-bus system. These objectives include fuel cost, fuel cost with valve loading effects, power losses, emissions, and voltage profile. Additionally, the algorithm's effectiveness is further assessed by testing it on single objective functions using medium and large-scale IEEE 57 and 118-bus networks.The results obtained by the proposed MAHA demonstrate its power and superiority for solving the OPF problem as well as the standard benchmark functions , surpassing the performance of other reported techniques.

  • 3.
    Hussien, Abdelazim
    et al.
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt.
    Pop, Adrian
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten.
    Kumar, Sumit
    Univ Tasmania, Australia.
    Hashim, Fatma A.
    Helwan Univ, Egypt; Middle East Univ, Jordan.
    Hu, Gang
    Xian Univ Technol, Peoples R China.
    A Novel Artificial Electric Field Algorithm for Solving Global Optimization and Real-World Engineering Problems2024Ingår i: BIOMIMETICS, ISSN 2313-7673, Vol. 9, nr 3, artikel-id 186Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The Artificial Electric Field Algorithm (AEFA) stands out as a physics-inspired metaheuristic, drawing inspiration from Coulomb's law and electrostatic force; however, while AEFA has demonstrated efficacy, it can face challenges such as convergence issues and suboptimal solutions, especially in high-dimensional problems. To overcome these challenges, this paper introduces a modified version of AEFA, named mAEFA, which leverages the capabilities of Levy flights, simulated annealing, and the Adaptive s-best Mutation and Natural Survivor Method (NSM) mechanisms. While Levy flights enhance exploration potential and simulated annealing improves search exploitation, the Adaptive s-best Mutation and Natural Survivor Method (NSM) mechanisms are employed to add more diversity. The integration of these mechanisms in AEFA aims to expand its search space, enhance exploration potential, avoid local optima, and achieve improved performance, robustness, and a more equitable equilibrium between local intensification and global diversification. In this study, a comprehensive assessment of mAEFA is carried out, employing a combination of quantitative and qualitative measures, on a diverse range of 29 intricate CEC'17 constraint benchmarks that exhibit different characteristics. The practical compatibility of the proposed mAEFA is evaluated on five engineering benchmark problems derived from the civil, mechanical, and industrial engineering domains. Results from the mAEFA algorithm are compared with those from seven recently introduced metaheuristic algorithms using widely adopted statistical metrics. The mAEFA algorithm outperforms the LCA algorithm in all 29 CEC'17 test functions with 100% superiority and shows better results than SAO, GOA, CHIO, PSO, GSA, and AEFA in 96.6%, 96.6%, 93.1%, 86.2%, 82.8%, and 58.6% of test cases, respectively. In three out of five engineering design problems, mAEFA outperforms all the compared algorithms, securing second place in the remaining two problems. Results across all optimization problems highlight the effectiveness and robustness of mAEFA compared to baseline metaheuristics. The suggested enhancements in AEFA have proven effective, establishing competitiveness in diverse optimization problems.

  • 4.
    Braik, Malik
    et al.
    Al Balqa Appl Univ, Jordan.
    Awadallah, Mohammed A.
    Al Aqsa Univ, Palestine; Ajman Univ, U Arab Emirates.
    Alzoubi, Hussein
    Yarmouk Univ, Jordan.
    Al-Hiary, Heba
    Al Balqa Appl Univ, Jordan.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Appl Sci Private Univ, Jordan; Middle East Univ, Jordan.
    Adaptive dynamic elite opposition-based Ali Baba and the forty thieves algorithm for high-dimensional feature selection2024Ingår i: Cluster Computing, ISSN 1386-7857, E-ISSN 1573-7543Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    High-dimensional Feature Selection Problems (HFSPs) have grown in popularity but remain challenging. When faced with such complex situations, the majority of currently employed Feature Selection (FS) methods for these problems drastically underperform in terms of effectiveness. To address HFSPs, a new Binary variant of the Ali Baba and the Forty Thieves (BAFT) algorithm known as binary adaptive elite opposition-based AFT (BAEOAFT), incorporating historical information and dimensional mutation is presented. The entire population is dynamically separated into two subpopulations in order to maintain population variety, and information and knowledge about individuals are extracted to offer adaptive and dynamic strategies in both subpopulations. Based on the individuals' history knowledge, Adaptive Tracking Distance (ATD) and Adaptive Perceptive Possibility (APP) schemes are presented for the exploration and exploitation subpopulations. A dynamic dimension mutation technique is used in the exploration subpopulation to enhance BAEOAFT's capacity in solving HFSPs. Meanwhile, the exploratory subpopulation uses Dlite Dynamic opposite Learning (EDL) to promote individual variety. Even if the exploitation group prematurely converges, the exploration subpopulation's variety can still be preserved. The proposed BAEOAFT-based FS technique was assessed by utilizing the k-nearest neighbor classifier on 20 HFSPs obtained from the UCI repository. The developed BAEOAFT achieved classification accuracy rates greater than those of its competitors and the conventional BAFT in more than 90% of the applied datasets. Additionally, BAEOAFT outperformed its rivals in terms of reduction rates while selecting the fewest number of features.

  • 5.
    Mostafa, Reham R.
    et al.
    Univ Sharjah, U Arab Emirates; Mansoura Univ, Egypt.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Appl Sci Private Univ, Jordan.
    Gaheen, Marwa A.
    Damietta Univ, Egypt.
    Ewees, Ahmed A.
    Damietta Univ, Egypt.
    Hashim, Fatma A.
    Helwan Univ, Egypt; Middle East Univ, Jordan.
    AEOWOA: hybridizing whale optimization algorithm with artificial ecosystem-based optimization for optimal feature selection and global optimization2024Ingår i: Evolving Systems, ISSN 1868-6478, E-ISSN 1868-6486Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The process of data classification involves determining the optimal number of features that lead to high accuracy. However, feature selection (FS) is a complex task that necessitates robust metaheuristics due to its challenging NP-hard nature. This paper introduces a hybrid algorithm that combines the Artificial Ecosystem Optimization (AEO) operators with the Whale Optimization Algorithm (WOA) to enhance numerical optimization and FS. While the WOA algorithm, inspired by the hunting behavior of whales, has been successful in solving various optimization problems, it can sometimes be limited in its ability to explore and may become trapped in local optima. To address this limitation, the authors propose the use of AEO operators to improve the exploration process of the WOA algorithm. The authors conducted experiments to evaluate the effectiveness of their proposed method, called AEOWOA, using the CEC'20 test suite for numerical optimization and sixteen datasets for FS. They compared the results with those obtained from other optimization methods. Through experimental and statistical analyses, it was observed that AEOWOA delivers efficient search results with faster convergence, reducing the feature size by up to 89% while achieving up to 94% accuracy. These findings shed light on potential future research directions in this field.

  • 6.
    Lu, Jing
    et al.
    Shangluo Univ, Peoples R China.
    Yang, Rui
    Xian Univ Technol, Peoples R China.
    Hu, Gang
    Xian Univ Technol, Peoples R China.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt.
    Ameliorated Snake Optimizer-Based Approximate Merging of Disk Wang-Ball Curves2024Ingår i: BIOMIMETICS, ISSN 2313-7673, Vol. 9, nr 3, artikel-id 134Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A method for the approximate merging of disk Wang-Ball (DWB) curves based on the modified snake optimizer (BEESO) is proposed in this paper to address the problem of difficulties in the merging of DWB curves. By extending the approximate merging problem for traditional curves to disk curves and viewing it as an optimization problem, an approximate merging model is established to minimize the merging error through an error formulation. Considering the complexity of the model built, a BEESO with better convergence accuracy and convergence speed is introduced, which combines the snake optimizer (SO) and three strategies including bi-directional search, evolutionary population dynamics, and elite opposition-based learning. The merging results and merging errors of numerical examples demonstrate that BEESO is effective in solving approximate merging models, and it provides a new method for the compression and transfer of product shape data in Computer-Aided Geometric Design.

  • 7.
    Mostafa, Reham R.
    et al.
    Univ Sharjah, U Arab Emirates; Mansoura Univ, Egypt.
    Houssein, Essam H.
    Minia Univ, Egypt.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Middle East Univ, Jordan; Appl Sci Private Univ, Jordan.
    Singh, Birmohan
    St Longowal Inst Engn & Technol, India.
    Emam, Marwa M.
    Minia Univ, Egypt.
    An enhanced chameleon swarm algorithm for global optimization and multi-level thresholding medical image segmentation2024Ingår i: Neural Computing & Applications, ISSN 0941-0643, E-ISSN 1433-3058Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Medical image segmentation is crucial in using digital images for disease diagnosis, particularly in post-processing tasks such as analysis and disease identification. Segmentation of magnetic resonance imaging (MRI) and computed tomography images pose distinctive challenges attributed to factors such as inadequate illumination during the image acquisition process. Multilevel thresholding is a widely adopted method for image segmentation due to its effectiveness and ease of implementation. However, the primary challenge lies in selecting the optimal set of thresholds to achieve accurate segmentation. While Otsu's between-class variance and Kapur's entropy assist in identifying optimal thresholds, their application to cases requiring more than two thresholds can be computationally intensive. Meta-heuristic algorithms are commonly employed in literature to calculate the threshold values; however, they have limitations such as a lack of precise convergence and a tendency to become stuck in local optimum solutions. In this paper, we introduce an improved chameleon swarm algorithm (ICSA) to address these limitations. ICSA is designed for image segmentation and global optimization tasks, aiming to improve the precision and efficiency of threshold selection in medical image segmentation. ICSA introduces the concept of the "best random mutation strategy" to enhance the search capabilities of the standard chameleon swarm algorithm (CSA). This strategy leverages three distribution functions-Levy, Gaussian, and Cauchy-for mutating search individuals. These diverse distributions contribute to improved solution quality and help prevent premature convergence. We conduct comprehensive experiments using the IEEE CEC'20 complex optimization benchmark test suite to evaluate ICSA's performance. Additionally, we employ ICSA in image segmentation, utilizing Otsu's approach and Kapur's entropy as fitness functions to determine optimal threshold values for a set of MRI images. Comparative analysis reveals that ICSA outperforms well-known metaheuristic algorithms when applied to the CEC'20 test suite and significantly improves image segmentation performance, proving its ability to avoid local optima and overcome the original algorithm's drawbacks. Medical image segmentation is essential for employing digital images for disease diagnosis, particularly for post-processing activities such as analysis and disease identification. Due to poor illumination and other acquisition-related difficulties, radiologists are especially concerned about the optimal segmentation of brain magnetic resonance imaging (MRI). Multilevel thresholding is the most widely used image segmentation method due to its efficacy and simplicity of implementation. The issue, however, is selecting the optimum set of criteria to effectively segment each image. Although methods like Otsu's between-class variance and Kapur's entropy help locate the optimal thresholds, using them for more than two thresholds requires a significant amount of processing resources. Meta-heuristic algorithms are commonly employed in literature to calculate the threshold values; however, they have limitations such as a lack of precise convergence and a tendency to become stuck in local optimum solutions. Due to the aforementioned, we present an improved chameleon swarm algorithm (ICSA) in this paper for image segmentation and global optimization tasks to be able to address these weaknesses. In the ICSA method, the best random mutation strategy has been introduced to improve the searchability of the standard CSA. The best random strategy utilizes three different types of distribution: Levy, Gaussian, and Cauchy to mutate the search individuals. These distributions have different functions, which help enhance the quality of the solutions and avoid premature convergence. Using the IEEE CEC'20 test suite as a recent complex optimization benchmark, a comprehensive set of experiments is carried out in order to evaluate the ICSA method and demonstrate the impact of combining the best random mutation strategy with the original CSA in improving both the performance of the solutions and the rate at which they converge. Furthermore, utilizing the Otsu approach and Kapur's entropy as a fitness function, ICSA is used as an image segmentation method to select the ideal threshold values for segmenting a set of MRI images. Within the experiments, the ICSA findings are compared with well-known metaheuristic algorithms. The comparative findings showed that ICSA performs better than other competitors in solving the CEC'20 test suite and has a significant performance boost in image segmentation.

  • 8.
    Hashim, Fatma A.
    et al.
    Helwan Univ, Egypt; Middle East Univ, Jordan.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Appl Sci Private Univ, Jordan.
    Bouaouda, Anas
    Hassan II Univ Casablanca, Morocco.
    Samee, Nagwan Abdel
    Princess Nourah bint Abdulrahman Univ, Saudi Arabia.
    Abu Khurma, Ruba
    Al Ahliyya Univ, Jordan.
    Alamro, Hayam
    Princess Nourah bint Abdulrahman Univ, Saudi Arabia.
    Al-Betar, Mohammed Azmi
    Ajman Univ, U Arab Emirates; Ajman Univ, U Arab Emirates.
    An enhanced exponential distribution optimizer and its application for multi-level medical image thresholding problems2024Ingår i: Alexandria Engineering Journal, ISSN 1110-0168, E-ISSN 2090-2670, Vol. 93, s. 142-188Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this paper, an enhanced version of the Exponential Distribution Optimizer (EDO) called mEDO is introduced to tackle global optimization and multi-level image segmentation problems. EDO is a math-inspired optimizer that has many limitations in handling complex multi -modal problems. mEDO tries to solve these drawbacks using 2 operators: phasor operator for diversity enhancement and an adaptive p -best mutation strategy for preventing it converging to local optima. To validate the effectiveness of the suggested optimizer, a comprehensive set of comparative experiments using the CEC'2020 test suite was conducted. The experimental results consistently prove that the suggested technique outperforms its counterparts in terms of both convergence speed and accuracy. Moreover, the suggested mEDO algorithm was applied for image segmentation using the multi-threshold image segmentation method with Otsu's entropy, providing further evidence of its enhanced performance. The algorithm was evaluated by comparing its results with those of existing well-known algorithms at various threshold levels. The experimental results validate that the proposed mEDO algorithm attains exceptional segmentation results for various threshold levels.

  • 9.
    Wang, Shuang
    et al.
    Putian Univ, Peoples R China; Fujian Prov Univ, Peoples R China; Sanming Univ, Peoples R China.
    Jia, Heming
    Sanming Univ, Peoples R China.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt.
    Abualigah, Laith
    Univ Tabuk, Saudi Arabia; Lebanese Amer Univ, Lebanon; Al Ahliyya Amman Univ, Jordan; Middle East Univ, Jordan; Yuan Ze Univ, Taiwan; Appl Sci Private Univ, Jordan.
    Lin, Guanjun
    Sanming Univ, Peoples R China.
    Wei, Hongwei
    Sanming Univ, Peoples R China.
    Lin, Zhenheng
    Putian Univ, Peoples R China; Fujian Prov Univ, Peoples R China.
    Dhal, Krishna Gopal
    Midnapore Coll Autonomous, India.
    Boosting aquila optimizer by marine predators algorithm for combinatorial optimization2024Ingår i: JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING, ISSN 2288-5048, Vol. 11, nr 2, s. 37-69Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this study, an improved version of aquila optimizer (AO) known as EHAOMPA has been developed by using the marine predators algorithm (MPA). MPA is a recent and well-behaved optimizer with a unique memory saving and fish aggregating devices mechanism. At the same time, it suffers from various defects such as inadequate global search, sluggish convergence, and stagnation of local optima. However, AO has contented robust global exploration capability, fast convergence speed, and high search efficiency. Thus, the proposed EHAOMPA aims to complement the shortcomings of AO and MPA while bringing new features. Specifically, the representative-based hunting technique is incorporated into the exploration stage to enhance population diversity. At the same time, random opposition-based learning is introduced into the exploitation stage to prevent the optimizer from sticking to local optima. This study tests the performance of EHAOMPA's on 23 standard mathematical benchmark functions, 29 complex test functions from the CEC2017 test suite, six constrained industrial engineering design problems, and a convolutional neural network hyperparameter (CNN-hyperparameter) optimization for Corona Virus Disease 19 (COVID-19) computed tomography-image detection problem. EHAOMPA is compared with four existing optimization algorithm types, achieving the best performance on both numerical and practical issues. Compared with other methods, the test function results demonstrate that EHAOMPA exhibits a more potent global search capability, a higher convergence rate, increased accuracy, and an improved ability to avoid local optima. The excellent experimental results in practical problems indicate that the developed EHAOMPA has great potential in solving real-world optimization problems. The combination of multiple strategies can effectively improve the performance of the algorithm. The source code of the EHAOMPA is publicly available at https://github.com/WangShuang92/EHAOMPA. Graphical Abstract

  • 10.
    Neggaz, Nabil
    et al.
    Univ Sci & Technol Oran Mohamed BOUDIAF, Algeria; Fac Math & Informat, Algeria.
    Neggaz, Imene
    Univ Sci & Technol Oran Mohamed BOUDIAF, Algeria; Fac Math & Informat, Canada; Ecole Super Informat Sidi Bel Abbes, Algeria.
    Abd Elaziz, Mohamed
    Zagazig Univ, Egypt; Lebanese Amer Univ, Lebanon; Galala Univ, Egypt; Ajman Univ, U Arab Emirates.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt.
    Abulaigh, Laith
    Univ Tabuk, Saudi Arabia; Al Ahliyya Amman Univ, Jordan; Middle East Univ, Jordan; Lebanese Amer Univ, Lebanon; Appl Sci Private Univ, Jordan; Yuan Ze Univ, Taiwan.
    Damasevicius, Robertas
    Silesian Tech Univ, Poland.
    Hu, Gang
    Xian Univ Technol, Peoples R China.
    Boosting manta rays foraging optimizer by trigonometry operators: a case study on medical dataset2024Ingår i: Neural Computing & Applications, ISSN 0941-0643, E-ISSN 1433-3058Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The selection of attributes has become a crucial research focus in the domains of pattern recognition, machine learning, and big data analysis. In essence, the contemporary challenge revolves around reducing dimensionality while maintaining both a quick response time and improved classification performance. Metaheuristics algorithms (MAs) have emerged as pivotal tools in addressing this issue. Firstly, the problem of attribute selection was approached using the manta ray foraging optimization (MRFO) approach, but the majority of MAs suffer from a problem of convergence toward local minima. To mitigate this challenge, an enhanced variant of MRFO, known as MRFOSCA, employs trigonometric operators inspired by the sine cosine algorithm (SCA) to tackle the feature selection problem. The k-nearest neighbor (k-NN) technique is employed for feature-set selection. Additionally, the statistical significance of the proposed algorithms is assessed using the nonparametric Wilcoxon's rank-sum test at a 5% significance level. The outcomes are assessed and compared against some well-known MAs, including the original MRFO and SCA, as well as Harris Hawks optimizer, dragonfly algorithm, grasshopper optimizer algorithm, whale optimizer algorithm, salp swarm algorithm, and grey wolf optimizer. The experimental and comparison analyses validate the pretty effective performance of the proposed methods on low- and high-dimensional datasets by providing the highest accuracy in 85% of the feature selection benchmarks.

  • 11.
    Adegboye, Oluwatayomi Rereloluwa
    et al.
    Univ Mediterranean Karpasia, Turkiye.
    Feda, Afi Kekeli
    European Univ Lefke, Turkiye.
    Ojekemi, Opeoluwa Seun
    Univ Mediterranean Karpasia, Turkiye.
    Agyekum, Ephraim Bonah
    Ural Fed Univ, Russia.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Appl Sci Private Univ, Jordan; Middle East Univ, Jordan.
    Kamel, Salah
    Aswan Univ, Egypt.
    Chaotic opposition learning with mirror reflection and worst individual disturbance grey wolf optimizer for continuous global numerical optimization2024Ingår i: Scientific Reports, E-ISSN 2045-2322, Vol. 14, nr 1, artikel-id 4660Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The effective meta-heuristic technique known as the grey wolf optimizer (GWO) has shown its proficiency. However, due to its reliance on the alpha wolf for guiding the position updates of search agents, the risk of being trapped in a local optimal solution is notable. Furthermore, during stagnation, the convergence of other search wolves towards this alpha wolf results in a lack of diversity within the population. Hence, this research introduces an enhanced version of the GWO algorithm designed to tackle numerical optimization challenges. The enhanced GWO incorporates innovative approaches such as Chaotic Opposition Learning (COL), Mirror Reflection Strategy (MRS), and Worst Individual Disturbance (WID), and it's called CMWGWO. MRS, in particular, empowers certain wolves to extend their exploration range, thus enhancing the global search capability. By employing COL, diversification is intensified, leading to reduced solution stagnation, improved search precision, and an overall boost in accuracy. The integration of WID fosters more effective information exchange between the least and most successful wolves, facilitating a successful exit from local optima and significantly enhancing exploration potential. To validate the superiority of CMWGWO, a comprehensive evaluation is conducted. A wide array of 23 benchmark functions, spanning dimensions from 30 to 500, ten CEC19 functions, and three engineering problems are used for experimentation. The empirical findings vividly demonstrate that CMWGWO surpasses the original GWO in terms of convergence accuracy and robust optimization capabilities.

  • 12.
    Ekinci, Serdar
    et al.
    Batman Univ, Turkiye.
    Izci, Davut
    Batman Univ, Turkiye.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Appl Sci Private Univ, Jordan; Middle East Univ, Jordan; Appl Sci Private Univ, Jordan.
    Comparative analysis of the hybrid gazelle-Nelder-Mead algorithm for parameter extraction and optimization of solar photovoltaic systems2024Ingår i: IET Renewable Power Generation, ISSN 1752-1416, E-ISSN 1752-1424Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The pressing need for sustainable energy solutions has driven significant research in optimizing solar photovoltaic (PV) systems which is crucial for maximizing energy conversion efficiency. Here, a novel hybrid gazelle-Nelder-Mead (GOANM) algorithm is proposed and evaluated. The GOANM algorithm synergistically integrates the gazelle optimization algorithm (GOA) with the Nelder-Mead (NM) algorithm, offering an efficient and powerful approach for parameter extraction in solar PV models. This investigation involves a thorough assessment of the algorithm's performance across diverse benchmark functions, including unimodal, multimodal, fixed-dimensional multimodal, and CEC2020 benchmark functions. Notably, the GOANM consistently outperforms other optimization approaches, demonstrating enhanced convergence speed, accuracy, and reliability. Furthermore, the application of the GOANM is extended to the parameter extraction of the single diode and double diode models of RTC France solar cell and PV model of Photowatt-PWP201 PV module. The experimental results consistently demonstrate that the GOANM outperforms other optimization approaches in terms of accurate parameter estimation, low root mean square values, fast convergence, and alignment with experimental data. These results emphasize its role in achieving superior performance and efficiency in renewable energy systems. This study compares a new hybrid Gazelle-Nelder-Mead (GOANM) algorithm for parameter extraction in solar cells, evaluating single diode (SDM) and double diode (DDM) models. GOANM's performance is first tested on CEC2020 and classical benchmark functions. Then, it optimizes SDM and DDM for the RTC France solar cell and Photowatt-PWP201 PV module. Results demonstrate the algorithm's superior performance. image

  • 13.
    Izci, Davut
    et al.
    Batman Univ, Turkiye; Middle East Univ, Jordan.
    Ekinci, Serdar
    Batman Univ, Turkiye.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Appl Sci Private Univ, Jordan.
    Efficient parameter extraction of photovoltaic models with a novel enhanced prairie dog optimization algorithm2024Ingår i: Scientific Reports, E-ISSN 2045-2322, Vol. 14, nr 1, artikel-id 7945Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The growing demand for solar energy conversion underscores the need for precise parameter extraction methods in photovoltaic (PV) plants. This study focuses on enhancing accuracy in PV system parameter extraction, essential for optimizing PV models under diverse environmental conditions. Utilizing primary PV models (single diode, double diode, and three diode) and PV module models, the research emphasizes the importance of accurate parameter identification. In response to the limitations of existing metaheuristic algorithms, the study introduces the enhanced prairie dog optimizer (En-PDO). This novel algorithm integrates the strengths of the prairie dog optimizer (PDO) with random learning and logarithmic spiral search mechanisms. Evaluation against the PDO, and a comprehensive comparison with eighteen recent algorithms, spanning diverse optimization techniques, highlight En-PDO's exceptional performance across different solar cell models and CEC2020 functions. Application of En-PDO to single diode, double diode, three diode, and PV module models, using experimental datasets (R.T.C. France silicon and Photowatt-PWP201 solar cells) and CEC2020 test functions, demonstrates its consistent superiority. En-PDO achieves competitive or superior root mean square error values, showcasing its efficacy in accurately modeling the behavior of diverse solar cells and performing optimally on CEC2020 test functions. These findings position En-PDO as a robust and reliable approach for precise parameter estimation in solar cell models, emphasizing its potential and advancements compared to existing algorithms.

  • 14.
    Hussien, Abdelazim
    et al.
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Appl Sci Private Univ, Jordan.
    Abd El-Sattar, Hoda
    Higher Inst Engn & Technol, Egypt.
    Hashim, Fatma A.
    Helwan Univ, Egypt; Middle East Univ, Jordan.
    Kamel, Salah
    Aswan Univ, Egypt.
    Enhancing optimal sizing of stand-alone hybrid systems with energy storage considering techno-economic criteria based on a modified artificial rabbits optimizer2024Ingår i: Journal of Energy Storage, ISSN 2352-152X, E-ISSN 2352-1538, Vol. 78, artikel-id 109974Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper examines and analyses a novel developed algorithm named Modified Artificial Rabbits Optimization (mARO), which is based on the modification of a bio-inspired meta-heuristic algorithm called Artificial Rabbits Optimization (ARO) combined with dimension learning-based hunting technique. In order to prove the efficiency and evaluate the constrained optimization of this modified algorithm mARO, it is applied to one of an engineering application. This engineering application is the study of the optimal sizing of a stand-alone hybrid system based on techno-economic criteria; this hybrid system consists of the PV, WT, Biomass system, and Battery units. The simulation of hybrid power systems must be carried out with the least amount of expense and harm to the environment so appropriate performance may be ensured using an efficient and optimal sizing strategy. In order to prove the superiority of this modified algorithm mARO, the results of this algorithm were compared with other new algorithms, and these algorithms are the original ARO, Dandelion Optimizer (DO), and Driving TrainingBased Optimization (DTOB).

  • 15.
    Jia, Heming
    et al.
    Sanming Univ, Peoples R China.
    Zhou, Xuelian
    Sanming Univ, Peoples R China.
    Zhang, Jinrui
    Sanming Univ, Peoples R China.
    Abualigah, Laith
    Al Ahliyya Amman Univ, Jordan; Middle East Univ, Jordan.
    Yildiz, Ali Riza
    Bursa Uludag Univ, Turkiye.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten.
    Modified crayfish optimization algorithm for solving multiple engineering application problems2024Ingår i: Artificial Intelligence Review, ISSN 0269-2821, E-ISSN 1573-7462, Vol. 57, nr 5, artikel-id 127Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Crayfish Optimization Algorithm (COA) is innovative and easy to implement, but the crayfish search efficiency decreases in the later stage of the algorithm, and the algorithm is easy to fall into local optimum. To solve these problems, this paper proposes an modified crayfish optimization algorithm (MCOA). Based on the survival habits of crayfish, MCOA proposes an environmental renewal mechanism that uses water quality factors to guide crayfish to seek a better environment. In addition, integrating a learning strategy based on ghost antagonism into MCOA enhances its ability to evade local optimality. To evaluate the performance of MCOA, tests were performed using the IEEE CEC2020 benchmark function and experiments were conducted using four constraint engineering problems and feature selection problems. For constrained engineering problems, MCOA is improved by 11.16%, 1.46%, 0.08% and 0.24%, respectively, compared with COA. For feature selection problems, the average fitness value and accuracy are improved by 55.23% and 10.85%, respectively. MCOA shows better optimization performance in solving complex spatial and practical application problems. The combination of the environment updating mechanism and the learning strategy based on ghost antagonism significantly improves the performance of MCOA. This discovery has important implications for the development of the field of optimization.

  • 16.
    Xiao, Yaning
    et al.
    Southern Univ Sci & Technol, Peoples R China.
    Cui, Hao
    Northeast Forestry Univ, Peoples R China.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Appl Sci Private Univ, Jordan.
    Hashim, Fatma A.
    Helwan Univ, Egypt; Middle East Univ, Jordan.
    MSAO: A multi-strategy boosted snow ablation optimizer for global optimization and real-world engineering applications2024Ingår i: Advanced Engineering Informatics, ISSN 1474-0346, E-ISSN 1873-5320, Vol. 61, artikel-id 102464Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Snow Ablation Optimizer (SAO) is a cutting-edge nature-inspired meta-heuristic technique that mimics the sublimation and melting processes of snow in its quest for optimal solution to complex problems. While SAO has demonstrated competitive performance in comparison to classical algorithms in early research, it still exhibits certain limitations including low convergence accuracy, a lack of population diversity, and premature convergence, particularly when addressing high-dimensional intricate challenges. To mitigate the above-mentioned adverse factors, this paper introduces a novel variant of SAO with featuring four enhancement strategies collectively referred as MSAO. Firstly, the good point set initialization strategy is employed to generate a uniformly distributed high-quality population, which facilitates the algorithm to enter the appropriate search domain rapidly. Secondly, the greedy selection method is adopted to reserve better candidate solutions for the next iteration, thus striking a robust exploration-exploitation balance. Then, the Differential Evolution (DE) scheme is introduced to expand the search range and enhance the exploitation capability of the algorithm for higher convergence accuracy. Finally, to reduce the risk of falling into local optima, a Dynamic Lens OppositionBased Learning (DLOBL) strategy is developed to operate on the current optimal solution dimension by dimension. With the blessing of these strategies, the optimization performance of MSAO is comprehensively improved. To comprehensively evaluate the optimization performance of MSAO, a series of numerical optimization experiments are conducted using the IEEE CEC2017 & CEC2022 test sets. In the IEEE CEC2017 experiments, the optimal crossover probability CR = 0.8 is determined and the effectiveness of each improvement strategy is ablatively verified. MSAO is compared with the basic SAO, various state-of-the-art optimizers, and CEC2017 champion algorithms in terms of solution accuracy, convergence speed, robustness, and scalability. In the IEEE CEC2022 experiments, MSAO is compared with some recently developed improved algorithms to further validate its superiority. The results demonstrate that MSAO has excellent overall optimization performance, with the smallest Friedman mean rankings of 1.66 and 1.25 on both test suites, respectively. In the majority of test cases, MSAO can provide more accurate and reliable solutions than other competitors. Furthermore, six realistic constrained engineering design challenges and one photovoltaic model parameter estimation issue are employed to demonstrate the practicality of MSAO. Our findings suggest that MSAO has excellent optimization capacity and broad application potential.

  • 17.
    Cui, Hao
    et al.
    Northeast Forestry Univ, Peoples R China.
    Xiao, Yaning
    Southern Univ Sci & Technol, Peoples R China.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Middle East Univ, Jordan; Appl Sci Private Univ, Jordan.
    Guo, Yanling
    Northeast Forestry Univ, Peoples R China.
    Multi-strategy boosted Aquila optimizer for function optimization and engineering design problems2024Ingår i: Cluster Computing, ISSN 1386-7857, E-ISSN 1573-7543Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    As the complexity of optimization problems continues to rise, the demand for high-performance algorithms becomes increasingly urgent. This paper addresses the challenges faced by the Aquila Optimizer (AO), a novel swarm-based intelligent optimizer simulating the predatory behaviors of Aquila in North America. While AO has shown good performance in prior studies, it grapples with issues such as poor convergence accuracy and a tendency to fall into local optima when tackling complex optimization tasks. To overcome these challenges, this paper proposes a multi-strategy boosted AO algorithm (PGAO) aimed at providing enhanced reliability for global optimization. The proposed algorithm incorporates several key strategies. Initially, a chaotic map is employed to initialize the positions of all search agents, enriching population diversity and laying a solid foundation for global exploration. Subsequently, the pinhole imaging learning strategy is introduced to identify superior candidate solutions in the opposite direction of the search domain during each iteration, accelerating convergence and increasing the probability of obtaining the global optimal solution. To achieve a more effective balance between the exploration and development phases in AO, a nonlinear switching factor is designed to replace the original fixed switching mechanism. Finally, the golden sine operator is utilized to enhance the algorithm's local exploitation trends. Through these four improvement strategies, the optimization performance of AO is significantly enhanced. The proposed PGAO algorithm's effectiveness is validated across 23 classical, 29 IEEE CEC2017, and 10 IEEE CEC2019 benchmark functions. Additionally, six real-world engineering design problems are employed to assess the practicability of PGAO. Results demonstrate that PGAO exhibits better competitiveness and application prospects compared to the basic method and various advanced algorithms. In conclusion, this study contributes to addressing the challenges of complex optimization problems, significantly improving the performance of global optimization algorithms, and holds both theoretical and practical significance.

  • 18.
    Neggaz, Nabil
    et al.
    Univ Sci Technol Oran Mohamed Boudiaf USTO MB, Algeria.
    Seyyedabbasi, Amir
    Istinye Univ, Turkiye.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Middle East Univ, Jordan; Appl Sci Private Univ, Jordan.
    Rahim, Mekki
    Univ Sci Technol Oran Mohamed Boudiaf USTO MB, Algeria.
    Beskirli, Mehmet
    Karamanoglu Mehmetbey Univ, Turkiye.
    Optimal Nodes Localization in Wireless Sensor Networks Using Nutcracker Optimizer Algorithms: Istanbul Area2024Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 12, s. 67986-68002Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Node localization is a non-deterministic polynomial time (NP-hard) problem in Wireless Sensor Networks (WSN). It involves determining the geographical position of each node in the network. For many applications in WSNs, such as environmental monitoring, security monitoring, health monitoring, and agriculture, precise location of nodes is crucial. As a result of this study, we propose a novel and efficient way to solve this problem without any regard to the environment, as well as without predetermined conditions. This proposed method is based on new proposed Nutcracker Optimization Algorithm (NOA). By utilizing this algorithm, it is possible to maximize coverage rates, decrease node numbers, and maintain connectivity. Several algorithms were used in this study, such as Grey Wolf Optimization (GWO), Kepler Optimization Algorithms (KOA), Harris Hawks Optimizer (HHO), Gradient-Based Optimizer (GBO) and Gazelle Optimization Algorithm (GOA). The node localization was first tested in Istanbul, Turkey, where it was determined to be a suitable study area. As a result of the metaheuristic-based approach and distributed architecture, the study is scalable to large-scale networks. Among these metaheuristic algorithms, NOA, KOA, and GWO have achieved significant performance in terms of coverage rates (CR), achieving coverage rates of 96.15%, 87.76%, and 93.49%, respectively. In terms of their ability to solve sensor node localization problems, these algorithms have proven to be effective.

  • 19.
    Das, Arunita
    et al.
    Midnapore Coll Autonomous, India.
    Sasmal, Buddhadev
    Midnapore Coll Autonomous, India; Midnapore City Coll, India.
    Dhal, Krishna Gopal
    Midnapore Coll Autonomous, India.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Middle East Univ, Jordan; Appl Sci Private Univ, Jordan.
    Naskar, Prabir Kumar
    Govt Coll Engn & Text Technol, India.
    Particle Swarm Optimizer Variants for Multi-level Thresholding: Theory, Performance Enhancement and Evaluation2024Ingår i: Archives of Computational Methods in Engineering, ISSN 1134-3060, E-ISSN 1886-1784Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Multilevel thresholding (MLT) has a significant impact in the realm of image segmentation. It is a simple and efficient method for image segmentation. However, as the number of thresholds increases, the computational complexity of MLT increases exponentially, and proper thresholding crucially depends on the utilized objective function. Swarm intelligence (SI) with the optimal objective function is used to enhance the efficacy of MLT image segmentation. Among the various SI techniques, Particle Swarm Optimization (PSO) and its variants are utilized extensively in literature to improve the performance of MLT. As a result, the objective of this study is to provide an updated survey on this topic. This study provides a comprehensive report of the classical PSO, and its improved variants, and their applications in MLT domains. However, like other SI techniques, PSO and its widely utilized variant called Darwinian PSO (DPSO) have the drawbacks of imbalanced exploration and exploitation and premature convergence. Therefore, along with the updated survey report, this study develops an efficient variant of the DPSO called Cauchy DPSO with Opposition Learning (CaDPSOOL). The Cauchy distribution and opposition learning (OL) have been incorporated to enhance the optimization capability by preventing premature convergence and maintaining the good balance between exploration and exploitation. Furthermore, a new hybrid objective function has also been designed by considering Otsu and Tsallis entropy in MLT domain for better segmentation results. The proposed CaDPSOOL with hybrid objective function has been employed for standard color image and hematopathology image segmentation domains. The results of this study show that the proposed model produces better results than other state-of-the-art MLT models in terms of segmentation quality metrics.

  • 20.
    Kalpana, Ponugoti
    et al.
    Vels Inst Sci Technol & Adv Studies, India.
    Anandan, R.
    Vels Inst Sci Technol & Adv Studies, India.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt.
    Migdady, Hazem
    Oman Coll Management & Technol, Oman.
    Abualigah, Laith
    Univ Tabuk, Saudi Arabia; Al Al Bayt Univ, Jordan; Al Ahliyya Amman Univ, Jordan; Middle East Univ, Jordan; Lebanese Amer Univ, Lebanon; Univ Sains Malaysia, Malaysia; Sunway Univ Malaysia, Malaysia; Appl Sci Private Univ, Jordan; Yuan Ze Univ, Taiwan.
    Plant disease recognition using residual convolutional enlightened Swin transformer networks2024Ingår i: Scientific Reports, E-ISSN 2045-2322, Vol. 14, nr 1, artikel-id 8660Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Agriculture plays a pivotal role in the economic development of a nation, but, growth of agriculture is affected badly by the many factors one such is plant diseases. Early stage prediction of these disease is crucial role for global health and even for game changers the farmer's life. Recently, adoption of modern technologies, such as the Internet of Things (IoT) and deep learning concepts has given the brighter light of inventing the intelligent machines to predict the plant diseases before it is deep-rooted in the farmlands. But, precise prediction of plant diseases is a complex job due to the presence of noise, changes in the intensities, similar resemblance between healthy and diseased plants and finally dimension of plant leaves. To tackle this problem, high-accurate and intelligently tuned deep learning algorithms are mandatorily needed. In this research article, novel ensemble of Swin transformers and residual convolutional networks are proposed. Swin transformers (ST) are hierarchical structures with linearly scalable computing complexity that offer performance and flexibility at various scales. In order to extract the best deep key-point features, the Swin transformers and residual networks has been combined, followed by Feed forward networks for better prediction. Extended experimentation is conducted using Plant Village Kaggle datasets, and performance metrics, including accuracy, precision, recall, specificity, and F1-rating, are evaluated and analysed. Existing structure along with FCN-8s, CED-Net, SegNet, DeepLabv3, Dense nets, and Central nets are used to demonstrate the superiority of the suggested version. The experimental results show that in terms of accuracy, precision, recall, and F1-rating, the introduced version shown better performances than the other state-of-art hybrid learning models.

  • 21.
    Hamadneh, Shereen
    et al.
    Al Al Bayt Univ, Jordan.
    Hamadneh, Jehan
    Jordan Univ Sci & Technol, Jordan.
    Alhenawi, Esraa
    Zarqa Univ, Jordan.
    Abu Khurma, Ruba
    Middle East Univ, Jordan; Appl Sci Private Univ, Jordan.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt.
    Predictive factors and adverse perinatal outcomes associated with maternal smoking status2024Ingår i: Scientific Reports, E-ISSN 2045-2322, Vol. 14, nr 1, artikel-id 3436Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    To identify risk factors for smoking among pregnant women, and adverse perinatal outcomes among pregnant women. A case-control study of singleton full-term pregnant women who gave birth at a university hospital in Jordan in June 2020. Pregnant women were divided into three groups according to their smoking status, active, passive, and non-smokers. They were interviewed using a semi-structured questionnaire that included demographic data, current pregnancy history, and neonatal outcomes. Low-level maternal education, unemployment, secondary antenatal care, and having a smoking husband were identified as risk factors for smoke exposure among pregnant women. The risk for cesarean section was ninefold higher in nulliparous smoking women. Women with low family income, those who did not receive information about the hazards of smoking, unemployed passive smoking women, and multiparty raised the risk of neonatal intensive care unit admission among active smoking women. This risk increased in active and passive women with lower levels of education, and inactive smoking women with low family income by 25 times compared to women with a higher level of education. Smoking is associated with adverse perinatal outcomes. Appropriate preventive strategies should address modifiable risk factors for smoking during pregnancy.

  • 22.
    Elseify, Mohamed A.
    et al.
    Al Azhar Univ, Egypt.
    Hashim, Fatma A.
    Helwan Univ, Egypt.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Middle East Univ, Jordan.
    Kamel, Salah
    Aswan Univ, Egypt.
    Single and multi-objectives based on an improved golden jackal optimization algorithm for simultaneous integration of multiple capacitors and multi-type DGs in distribution systems2024Ingår i: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 353, artikel-id 122054Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper proposes a novel placement technique based on the improved golden jackal optimization (IGJO) algorithm for multiple capacitor banks (CBs) and multi-type DGs in a distribution network considering single and multi-objective problems. The proposed algorithm incorporates memory-based equations and random walk strategy to enhance the performance of the recent golden jackal optimization in terms of accuracy and convergence speed. The optimization problem is formulated as a weighted multi-objective that seeks to enhance the voltage profiles, boost stability, and minimize the total active power loss. An index named reactive loss sensitivity (QLSI) is also employed with the developed IGJO to identify the candidate nodes for the DGs and CBs installation to reduce the search space of the optimization algorithm. The robustness of the developed IGJO algorithm is evaluated through the CEC 2020 benchmark functions, and a comparison study is conducted with the original GJO and the other nine fresh competitors using various statistical tests to confirm its dominance and superiority. Then, the proposed IGJO is implemented in single and multi-objectives for the optimal deployment of multiple CBs individually and simultaneously with multiple DGs with different operating modes to enhance the performance of the IEEE 69-bus radial distribution system (RDS). The fetched outcomes are compared with the original GJO, weevil optimizer algorithm (WeevilOA), skill optimization algorithm (SOA), and Tasmanian devil optimization (TDO) to further measure its efficacy using different statistical tests. The IGJO algorithm is also applied to deploy multiple DGs for the IEEE 118-bus RDS with the aim of minimizing active loss. The simulation findings affirmed that the proposed IGJO technique beats the other rivals in all investigated situations, qualifying for the optimal inclusion of DGs in the presence of generation and demand uncertainties. Specifically, the integration of three units of CBs synchronously with three DGs Type-I and DG Type-III reduces

  • 23.
    Ebeed, Mohamed
    et al.
    Univ Jaen, Spain; Sohag Univ, Egypt.
    Ali, Shimaa
    Sohag Univ, Egypt.
    Kassem, Ahmed M.
    Sohag Univ, Egypt.
    Hashem, Mohamed
    Holding Co Water & Wastewater, Egypt.
    Kamel, Salah
    Aswan Univ, Egypt.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Middle East Univ, Jordan.
    Jurado, Francisco
    Univ Jaen, Spain.
    Mohamed, Emad A.
    Prince Sattam Bin Abdulaziz Univ, Saudi Arabia.
    Solving stochastic optimal reactive power dispatch using an Adaptive Beluga Whale optimization considering uncertainties of renewable energy resources and the load growth2024Ingår i: Ain Shams Engineering Journal, ISSN 2090-4479, E-ISSN 2090-4495, Vol. 15, nr 7, artikel-id 102762Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The electrical system performance can be improved considerably by controlling the reactive power flow in the system. The reactive power control can be achieved by optimal reactive power dispatch (ORPD) problem solution and optimal integration of the FACTS devices. With high penetration of renewable energy sources (RESs) and the load growth, the ORPD solution became a challenging and a complex task due to the stochastic nature of the RERs and the load growth. In this regard, the aim of this paper is to solve the stochastic optimal reactive power dispatch (SORPD) with optimal inclusion of PV units, wind turbines and the unified power flow controller (UPFC) under uncertainties of the load growth and the generated powers. An Adaptive Beluga Whale Optimization (ABWO) is proposed for solving the SORPD which is based on the Fitness-Distance Balance Selection (FDBS) strategy and the territorial solitary males' strategy of the Mountain Gazelle Optimizer. The proposed ABWO is tested on IEEE 30-bus system and a comparison with other optimization techniques for solving the ordinary ORPD is presented for validating the proposed ABWO. The obtained results reveal that the TEPL is reduced from 5.3168 MW to 3.97985 MW with optimal integration of the RERs and UPFC. Likewise, the TEVD is reduced from 0.1794p.u. to 0.10689p.u. and the TVSI is decreased from 0.1289p.u. to 0.0476p.u.

  • 24.
    Alhenawi, Esra'a
    et al.
    Zarqa Univ, Jordan.
    Abu Khurma, Ruba
    Middle East Univ, Jordan.
    Damasevicius, Robertas
    Vytautas Magnus Univ, Lithuania.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Appl Sci Private Univ, Jordan.
    Solving Traveling Salesman Problem Using Parallel River Formation Dynamics Optimization Algorithm on Multi-core Architecture Using Apache Spark2024Ingår i: International Journal of Computational Intelligence Systems, ISSN 1875-6891, E-ISSN 1875-6883, Vol. 17, nr 1, artikel-id 4Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    According to Moore's law, computer processing hardware technology performance is doubled every year. To make effective use of this technological development, the algorithmic solutions have to be developed at the same speed. Consequently, it is necessary to design parallel algorithms to be implemented on parallel machines. This helps to exploit the multi-core environment by executing multiple instructions simultaneously on multiple processors. Traveling Salesman (TSP) is a challenging non-deterministic-hard optimization problem that has exponential running time using brute-force methods. TSP is concerned with finding the shortest path starting with a point and returning to that point after visiting the list of points, provided that these points are visited only once. Meta-heuristic optimization algorithms have been used to tackle TSP and find near-optimal solutions in a reasonable time. This paper proposes a parallel River Formation Dynamics Optimization Algorithm (RFD) to solve the TSP problem. The parallelization technique depends on dividing the population into different processors using the Map-Reduce framework in Apache Spark. The experiments are accomplished in three phases. The first phase compares the speedup, running time, and efficiency of RFD on 1 (sequential RFD), 4, 8, and 16 cores. The second phase compares the proposed parallel RFD with three parallel water-based algorithms, namely the Water Flow algorithm, Intelligent Water Drops, and the Water Cycle Algorithm. To achieve fairness, all algorithms are implemented using the same system specifications and the same values for shared parameters. The third phase compares the proposed parallel RFD with the reported results of metaheuristic algorithms that were used to solve TSP in the literature. The results demonstrate that the RFD algorithm has the best performance for the majority of problem instances, achieving the lowest running times across different core counts. Our findings highlight the importance of selecting the most suitable algorithm and core count based on the problem characteristics to achieve optimal performance in parallel optimization.

  • 25.
    Agyekum, Ephraim Bonah
    et al.
    Ural Fed Univ, Russia.
    Ampah, Jeffrey Dankwa
    Tianjin Univ, Peoples R China.
    Khan, Tahir
    Zhejiang Univ, Peoples R China.
    Giri, Nimay Chandra
    Centurion Univ Technol & Management, India; Centur Univ Technol & Managemention, India.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Appl Sci Private Univ, Jordan; Middle East Univ, Jordan.
    Velkin, Vladimir Ivanovich
    Ural Fed Univ, Russia.
    Mehmood, Usman
    Bahcesehir Cyprus Univ, Turkiye; Univ Punjab, Pakistan.
    Kamel, Salah
    Aswan Univ, Egypt.
    Towards a reduction of emissions and cost-savings in homes: Techno-economic and environmental impact of two different solar water heaters2024Ingår i: Energy Reports, E-ISSN 2352-4847, Vol. 11, s. 963-981Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    South Africa currently has the highest carbon emission intensity per kilowatt of electricity generation globally, and its government intends to reduce it. Some of the measures taken by the government include a reduction of emissions in the building sector using solar water heating (SWH) systems. However, there is currently no study in the country that comprehensively assesses the technical, economic, and environmental impact of SWH systems across the country. This study therefore used the System Advisor Model (SAM) to model two different technologies of SWH systems (i.e., flat plate (FPC) and evacuated tube (EPC) SWH) at five different locations (i.e., Pretoria, Upington, Kimberley, Durban, and Cape Town) strategically selected across the country. According to the study, the optimum azimuth for both the evacuated tube and flat plate SWH system in South Africa is 0 degrees. Installing FPC and EPC at the different locations would yield payback periods of 3.2 to 4.4 years and 3.5 to 4.3 years, respectively. Comparably, levelized cost of energy for the FPC and EPC will range from 7.47 to 9.62 cents/kWh and 7.66 to 9.24 cents/kWh, respectively, based on where the SWH system is located. Depending on where the facility is located, the annual cost savings for the FPC system would be between $486 and $625, while the EPC system would save between $529 and $638. Using SWHs can reduce CO2 emissions by 75-77% for the evacuated tube system and 69-76% for the flat plate system annually, depending on the location.

  • 26.
    Sasmal, Buddhadev
    et al.
    Midnapore Coll Autonomous, India.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Middle East Univ, Jordan.
    Das, Arunita
    Midnapore Coll Autonomous, India.
    Dhal, Krishna Gopal
    Midnapore Coll Autonomous, India.
    A Comprehensive Survey on Aquila Optimizer2023Ingår i: Archives of Computational Methods in Engineering, ISSN 1134-3060, E-ISSN 1886-1784, Vol. 30, s. 4449-4476Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Aquila Optimizer (AO) is a well-known nature-inspired optimization algorithm (NIOA) that was created in 2021 based on the prey grabbing behavior of Aquila. AO is a population-based NIOA that has demonstrated its effectiveness in the field of complex and nonlinear optimization in a short period of time. As a result, the purpose of this study is to provide an updated survey on the topic. This survey accurately reports on the designed enhanced AO variations and their applications. In order to properly assess AO, a rigorous comparison between AO and its peer NIOAs is conducted over mathematical benchmark functions. The experimental results show the AO provides competitive outcomes.

  • 27.
    Hussien, Abdelazim
    et al.
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt.
    Liang, Guoxi
    Wenzhou Polytech, Peoples R China.
    Chen, Huiling
    Wenzhou Univ, Peoples R China.
    Lin, Haiping
    Hangzhou Vocat & Tech Coll, Peoples R China.
    A Double Adaptive Random Spare Reinforced Sine Cosine Algorithm2023Ingår i: CMES - Computer Modeling in Engineering & Sciences, ISSN 1526-1492, E-ISSN 1526-1506, Vol. 136, nr 3, s. 2267-2289Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Many complex optimization problems in the real world can easily fall into local optimality and fail to find the optimal solution, so more new techniques and methods are needed to solve such challenges. Metaheuristic algorithms have received a lot of attention in recent years because of their efficient performance and simple structure. Sine Cosine Algorithm (SCA) is a recent Metaheuristic algorithm that is based on two trigonometric functions Sine & Cosine. However, like all other metaheuristic algorithms, SCA has a slow convergence and may fail in sub-optimal regions. In this study, an enhanced version of SCA named RDSCA is suggested that depends on two techniques: random spare/replacement and double adaptive weight. The first technique is employed in SCA to speed the convergence whereas the second method is used to enhance exploratory searching capabilities. To evaluate RDSCA, 30 functions from CEC 2017 and 4 real-world engineering problems are used. Moreover, a non parametric test called Wilcoxon signed-rank is carried out at 5% level to evaluate the significance of the obtained results between RDSCA and the other 5 variants of SCA. The results show that RDSCA has competitive results with other metaheuristics algorithms.

  • 28.
    Daqaq, Fatima
    et al.
    Mohammed V Univ Rabat, Morocco.
    Hassan, Mohamed H.
    Minist Elect & Renewable Energy, Egypt.
    Kamel, Salah
    Aswan Univ, Egypt.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Middle East Univ, Jordan.
    A leader supply-demand-based optimization for large scale optimal power flow problem considering renewable energy generations2023Ingår i: Scientific Reports, E-ISSN 2045-2322, Vol. 13, nr 1, artikel-id 14591Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The supply-demand-based optimization (SDO) is among the recent stochastic approaches that have proven its capability in solving challenging engineering tasks. Owing to the non-linearity and complexity of the real-world IEEE optimal power flow (OPF) in modern power system issues and like the existing algorithms, the SDO optimizer necessitates some enhancement to satisfy the required OPF characteristics integrating hybrid wind and solar powers. Thus, a SDO variant namely leader supply-demand-based optimization (LSDO) is proposed in this research. The LSDO is suggested to improve the exploration based on the simultaneous crossover and mutation mechanisms and thereby reduce the probability of trapping in local optima. The LSDO effectiveness has been first tested on 23 benchmark functions and has been assessed through a comparison with well-regarded state-of-the-art competitors. Afterward, Three well-known constrained IEEE 30, 57, and 118-bus test systems incorporating both wind and solar power sources were investigated in order to authenticate the performance of the LSDO considering a constraint handling technique called superiority of feasible solutions (SF). The statistical outcomes reveal that the LSDO offers promising competitive results not only for its first version but also for the other competitors.

  • 29.
    Wang, Shuang
    et al.
    Putian Univ, Peoples R China; Sanming Univ, Peoples R China.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt.
    Kumar, Sumit
    Univ Tasmania, Australia.
    AlShourbaji, Ibrahim
    Jazan Univ, Saudi Arabia.
    Hashim, Fatma A.
    Helwan Univ, Egypt; Middle East Univ, Jordan.
    A modified smell agent optimization for global optimization and industrial engineering design problems2023Ingår i: JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING, ISSN 2288-5048, Vol. 10, nr 6, s. 2147-2176Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper introduces an Improved Smell Agent Optimization Algorithm (mSAO), a new and enhanced metaheuristic designed to tackle complex engineering optimization issues by overcoming the shortcomings of the recently introduced Smell Agent Optimization Algorithm. The proposed mSAO incorporates the jellyfish swarm active-passive mechanism and novel random operator in the elementary SAO. The objective of modification is to improve the global convergence speed, exploration-exploitation behaviour, and performance of SAO, as well as provide a problem-free method of global optimization. For numerical validation, the mSAO is examined using 29 IEEE benchmarks with varying degrees of dimensionality, and the findings are contrasted with those of its basic version and numerous renowned recently developed metaheuristics. To measure the viability of the mSAO algorithm for real-world applications, the algorithm was employed to solve to resolve eight challenges drawn from real-world scenarios including cantilever beam design, multi-product batch plant, industrial refrigeration system, pressure vessel design, speed reducer design, tension/compression spring, and three-bar truss problem. The computational analysis demonstrates the robustness of mSAO relatively in finding optimal solutions for mechanical, civil, and industrial design problems. Experimental results show that the suggested modifications lead to an improvement in solution quality by 10-20% of basic SAO while solving constraint benchmarks and engineering problems. Additionally, it contributes to avoiding local optimal stuck, and premature convergence limitations of SAO and simultaneously

  • 30.
    Zheng, Rong
    et al.
    Putian Univ, Peoples R China; Sanming Univ, Peoples R China.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt.
    Qaddoura, Raneem
    Al Hussein Tech Univ, Jordan.
    Jia, Heming
    Sanming Univ, Peoples R China.
    Abualigah, Laith
    Al Al Bayt Univ, Jordan; Al Ahliyya Amman Univ, Jordan; Middle East Univ, Jordan; Appl Sci Private Univ, Jordan; Univ Sains Malaysia, Malaysia.
    Wang, Shuang
    Putian Univ, Peoples R China.
    Saber, Abeer
    Kafr El Sheikh Univ, Egypt.
    A multi-strategy enhanced African vultures optimization algorithm for global optimization problems2023Ingår i: JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING, ISSN 2288-5048, Vol. 10, nr 1, s. 329-356Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The African vultures optimization algorithm (AVOA) is a recently proposed metaheuristic inspired by the African vultures behaviors. Though the basic AVOA performs very well for most optimization problems, it still suffers from the shortcomings of slow convergence rate and local optimal stagnation when solving complex optimization tasks. Therefore, this study introduces a modified version named enhanced AVOA (EAVOA). The proposed EAVOA uses three different techniques namely representative vulture selection strategy, rotating flight strategy, and selecting accumulation mechanism, respectively, which are developed based on the basic AVOA. The representative vulture selection strategy strikes a good balance between global and local searches. The rotating flight strategy and selecting accumulation mechanism are utilized to improve the quality of the solution. The performance of EAVOA is validated on 23 classical benchmark functions with various types and dimensions and compared to those of nine other state-of-the-art methods according to numerical results and convergence curves. In addition, three real-world engineering design optimization problems are adopted to evaluate the practical applicability of EAVOA. Furthermore, EAVOA has been applied to classify multi-layer perception using XOR and cancer datasets. The experimental results clearly show that the EAVOA has superiority over other methods.

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  • 31.
    Saber, Abeer
    et al.
    Damietta Univ, Egypt.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Middle East Univ, Jordan.
    Awad, Wael A.
    Damietta Univ, Egypt.
    Mahmoud, Amena
    Kafr El Sheikh Univ, Egypt.
    Allakany, Alaa
    Kafr El Sheikh Univ, Egypt.
    Adapting the pre-trained convolutional neural networks to improve the anomaly detection and classification in mammographic images2023Ingår i: Scientific Reports, E-ISSN 2045-2322, Vol. 13, nr 1, artikel-id 14877Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Mortality from breast cancer (BC) is among the top causes of cancer death in women. BC can be effectively treated when diagnosed early, improving the likelihood that a patient will survive. BC masses and calcification clusters must be identified by mammography in order to prevent disease effects and commence therapy at an early stage. A mammography misinterpretation may result in an unnecessary biopsy of the false-positive results, lowering the patients odds of survival. This study intends to improve breast mass detection and identification in order to provide better therapy and reduce mortality risk. A new deep-learning (DL) model based on a combination of transfer-learning (TL) and long short-term memory (LSTM) is proposed in this study to adequately facilitate the automatic detection and diagnosis of the BC suspicious region using the 80-20 method. Since DL designs are modelled to be problem-specific, TL applies the knowledge gained during the solution of one problem to another relevant problem. In the presented model, the learning features from the pre-trained networks such as the squeezeNet and DenseNet are extracted and transferred with the features that have been extracted from the INbreast dataset. To measure the proposed model performance, we selected accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC) as our metrics of choice. The classification of mammographic data using the suggested model yielded overall accuracy, sensitivity, specificity, precision, and AUC values of 99.236%, 98.8%, 99.1%, 96%, and 0.998, respectively, demonstrating the models efficacy in detecting breast tumors.

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  • 32.
    Selim, Ali
    et al.
    Aswan Univ, Egypt.
    Hassan, Mohamed H.
    Minist Elect & Renewable Energy, Egypt; Univ Jaen, Spain.
    Kamel, Salah
    Aswan Univ, Egypt.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Middle East Univ, Jordan.
    Allocation of distributed generator in power networks through an enhanced jellyfish search algorithm2023Ingår i: Energy Reports, E-ISSN 2352-4847, Vol. 10, s. 4761-4780Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This study presents the Jellyfish Search Optimizer (JS), a novel metaheuristic optimization algorithm, for the efficient allocation of multi-type distributed generations (DGs) in distribution power systems. To further enhance the performance of the original JS algorithm, a leader-based mutation-selection approach called LJS is proposed to circumvent local optima. The effectiveness of LJS is evaluated with various benchmark problems and compared with other competitive optimization algorithms. Moreover, LJS is utilized to allocate different types of DGs (Type I, Type II, and Type III) in standard IEEE and practical Portuguese distribution systems. The opti-mization problem of the DG allocation is performed to minimize the power loss and enhance the voltage profile by minimizing the voltage deviation (VD) and maximizing the voltage stability index (VSI) as single-and multi-objectives optimization problems. The results obtained demonstrate the superior performance of LJS in achieving optimal solutions for benchmark problems as well as for the allocation of multi-type DGs. Notably, the inte-gration of DG Type III leads to a remarkable reduction in total power loss, achieving a reduction of 94.44 %, 98.10 % and 96.07 in IEEE 33-bus,IEEE 69-bus and 94 bus systems, respectively.

  • 33.
    Hashim, Fatma A.
    et al.
    Helwan Univ, Egypt; Middle East Univ, Jordan.
    Houssein, Essam H.
    Minia Univ, Egypt.
    Mostafa, Reham R.
    Univ Sharjah, U Arab Emirates; Mansoura Univ, Egypt.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt.
    Helmy, Fatma
    Misr Int Univ, Egypt.
    An efficient adaptive-mutated Coati optimization algorithm for feature selection and global optimization2023Ingår i: Alexandria Engineering Journal, ISSN 1110-0168, E-ISSN 2090-2670, Vol. 85, s. 29-48Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The feature selection (FS) problem has occupied a great interest of scientists lately since the highly dimensional datasets might have many redundant and irrelevant features. FS aims to eliminate such features and select the most important ones that affect classification performance. Metaheuristic algorithms are the best choice to solve this combinatorial problem. Recent researchers invented and adapted new algorithms, hybridized many algorithms, or enhanced existing ones by adding some operators to solve the FS problem. In our paper, we added some operators to the Coati optimization algorithm (CoatiOA). The first operator is the adaptive s-best mutation operator to enhance the balance between exploration and exploitation. The second operator is the directional mutation rule that opens the way to discover the search space thoroughly. The final enhancement is controlling the search direction toward the global best. We tested the proposed mCoatiOA algorithm in solving) in solving challenging problems from the CEC'20 test suite. mCoatiOA performance was compared with Dandelion Optimizer (DO), African vultures optimization algorithm (AVOA), Artificial gorilla troops optimizer (GTO), whale optimization algorithm (WOA), Fick's Law Algorithm (FLA), Particle swarm optimization (PSO), Harris hawks optimization (HHO), and Tunicate swarm algorithm (TSA). According to the average fitness, it can be observed that the proposed method, mCoatiOA, performs better than the other optimization algorithms on 8 test functions. It has lower average standard deviation values compared to the competitive algorithms. Wilcoxon test showed that the results obtained by mCoatiOA are significantly different from those of the other rival algorithms. mCoatiOA has been tested as a feature selection algorithm. Fifteen benchmark datasets of various types were collected from the UCI machine-learning repository. Different evaluation criteria are used to determine the effectiveness of the proposed method. The proposed mCoatiOA achieved better results in comparison with other published methods. It achieved the mean best results on 75% of the datasets.

  • 34.
    Alshourbaji, Ibrahim
    et al.
    Univ Hertfordshire, England; Jazan Univ, Saudi Arabia.
    Helian, Na
    Univ Hertfordshire, England.
    Sun, Yi
    Univ Hertfordshire, England.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt.
    Abualigah, Laith
    Al Al Bayt Univ, Jordan; Lebanese Amer Univ, Lebanon; Al Ahliyya Amman Univ, Jordan; Middle East Univ, Jordan; Appl Sci Private Univ, Jordan; Univ Sains Malaysia, Malaysia; Sunway Univ, Malaysia.
    Elnaim, Bushra
    Prince Sattam Bin Abdulaziz Univ, Saudi Arabia.
    An efficient churn prediction model using gradient boosting machine and metaheuristic optimization2023Ingår i: Scientific Reports, E-ISSN 2045-2322, Vol. 13, nr 1, artikel-id 14441Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Customer churn remains a critical challenge in telecommunications, necessitating effective churn prediction (CP) methodologies. This paper introduces the Enhanced Gradient Boosting Model (EGBM), which uses a Support Vector Machine with a Radial Basis Function kernel (SVMRBF) as a base learner and exponential loss function to enhance the learning process of the GBM. The novel base learner significantly improves the initial classification performance of the traditional GBM and achieves enhanced performance in CP-EGBM after multiple boosting stages by utilizing state-of-the-art decision tree learners. Further, a modified version of Particle Swarm Optimization (PSO) using the consumption operator of the Artificial Ecosystem Optimization (AEO) method to prevent premature convergence of the PSO in the local optima is developed to tune the hyper-parameters of the CP-EGBM effectively. Seven open-source CP datasets are used to evaluate the performance of the developed CP-EGBM model using several quantitative evaluation metrics. The results showed that the CP-EGBM is significantly better than GBM and SVM models. Results are statistically validated using the Friedman ranking test. The proposed CP-EGBM is also compared with recently reported models in the literature. Comparative analysis with state-of-the-art models showcases CP-EGBMs promising improvements, making it a robust and effective solution for churn prediction in the telecommunications industry.

  • 35.
    Izci, Davut
    et al.
    Batman Univ, Turkiye; Middle East Univ, Jordan.
    Ekinci, Serdar
    Batman Univ, Turkiye.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt.
    An elite approach to re-design Aquila optimizer for efficient AFR system control2023Ingår i: PLOS ONE, E-ISSN 1932-6203, Vol. 18, nr 9, artikel-id e0291788Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Controlling the air-fuel ratio system (AFR) in lean combustion spark-ignition engines is crucial for mitigating emissions and addressing climate change. In this regard, this study proposes an enhanced version of the Aquila optimizer (ImpAO) with a modified elite opposition-based learning technique to optimize the feedforward (FF) mechanism and proportional-integral (PI) controller parameters for AFR control. Simulation results demonstrate ImpAOs outstanding performance compared to state-of-the-art algorithms. It achieves a minimum cost function value of 0.6759, exhibiting robustness and stability with an average +/- standard deviation range of 0.6823 +/- 0.0047. The Wilcoxon signed-rank test confirms highly significant differences (p<0.001) between ImpAO and other algorithms. ImpAO also outperforms competitors in terms of elapsed time, with an average of 43.6072 s per run. Transient response analysis reveals that ImpAO achieves a lower rise time of 1.1845 s, settling time of 3.0188 s, overshoot of 0.1679%, and peak time of 4.0371 s compared to alternative algorithms. The algorithm consistently achieves lower error-based cost function values, indicating more accurate control. ImpAO demonstrates superior capabilities in tracking the desired input signal compared to other algorithms. Comparative assessment with recent metaheuristic algorithms further confirms ImpAOs superior performance in terms of transient response metrics and error-based cost functions. In summary, the simulation results provide strong evidence of the exceptional performance and effectiveness of the proposed ImpAO algorithm. It establishes ImpAO as a reliable and superior solution for optimizing the FF mechanism-supported PI controller for the AFR system, surpassing state-of-the-art algorithms and recent metaheuristic optimizers.

  • 36.
    Hussien, Abdelazim
    et al.
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Appl Sci Private Univ, Jordan.
    Kumar, Sumit
    Univ Tasmania, Australia.
    Singh, Simrandeep
    Chandigarh Univ, India.
    Pan, Jeng-Shyang
    Shandong Univ Sci & Technol, Peoples R China; Chaoyang Univ Technol, Taiwan.
    Hashim, Fatma A.
    Helwan Univ, Egypt; Middle East Univ, Jordan.
    An enhanced dynamic differential annealed algorithm for global optimization and feature selection2023Ingår i: JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING, ISSN 2288-5048, Vol. 11, nr 1, s. 49-72Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Dynamic differential annealed optimization (DDAO) is a recently developed physics-based metaheuristic technique that mimics the classical simulated annealing mechanism. However, DDAO has limited search abilities, especially when solving complicated and complex problems. A unique variation of DDAO, dubbed as mDDAO, is developed in this study, in which opposition-based learning technique and a novel updating equation are combined with DDAO. mDDAO is tested on 10 different functions from CEC2020 and compared with the original DDAO and nine other algorithms. The proposed mDDAO algorithm performance is evaluated using 10 numerical constrained functions from the recently released CEC 2020 benchmark suite, which includes a variety of dimensionally challenging optimisation tasks. Furthermore, to measure its viability, mDDAO is employed to solve feature selection problems using fourteen UCI datasets and a real-life Lymphoma diagnosis problem. Results prove that mDDAO has a superior performance and consistently outperforms counterparts across benchmarks, achieving fitness improvements ranging from 1% to 99.99%. In feature selection, mDDAO excels by reducing feature count by 23% to 79% compared to other methods, enhancing computational efficiency and maintaining classification accuracy. Moreover, in lymphoma diagnosis, mDDAO demonstrates up to 54% higher average fitness, 18% accuracy improvement, and 86% faster computation times. Graphical Abstract

  • 37.
    Hassan, Mohamed H.
    et al.
    Minist Elect & Renewable Energy, Egypt.
    Daqaq, Fatima
    Mohammed V Univ Rabat, Morocco.
    Kamel, Salah
    Aswan Univ, Egypt.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Middle East Univ, Jordan.
    Zawbaa, Hossam M.
    Beni Suef Univ, Egypt; Appl Sci Private Univ, Jordan.
    An enhanced hunter-prey optimization for optimal power flow with FACTS devices and wind power integration2023Ingår i: IET Generation, Transmission & Distribution, ISSN 1751-8687, E-ISSN 1751-8695, Vol. 17, nr 14, s. 3115-3139Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper proposes an improved version of the Hunter-prey optimization (HPO) method to enhance its search capabilities for solving the Optimal Power Flow (OPF) problem, which includes FACTS devices and wind power energy integration. The new algorithm is inspired by the behavior of predator and prey animals, such as lions, wolves, leopards, stags, and gazelles. The primary contribution of this study is to address the tendency of the original HPO approach to get trapped in local optima, by proposing an enhanced Hunter-prey optimization (EHPO) approach that improves both the exploration and exploitation phases. This is achieved through a random mutation for exploration and an adaptive process for exploitation, which balances the transition between the two phases. The performance of the EHPO algorithm is compared with other optimization algorithms, and subsequently, it is used to solve the OPF problem incorporating FACTS devices and wind power. The results demonstrate the effectiveness and superiority of the proposed algorithm. In conclusion, this study successfully enhances the EHPO algorithm to provide better accuracy and faster convergence in finding optimal solutions for complex real-world problems.

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  • 38.
    Al-Shourbaji, Ibrahim
    et al.
    Jazan Univ, Saudi Arabia; Univ Hertfordshire, England.
    Kachare, Pramod
    Ramrao Adik Inst Technol, India.
    Fadlelseed, Sajid
    Univ Hertfordshire, England.
    Jabbari, Abdoh
    Jazan Univ, Saudi Arabia.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt.
    Al-Saqqar, Faisal
    Al Al Bayt Univ, Jordan.
    Abualigah, Laith
    Al Al Bayt Univ, Jordan; Sunway Univ Malaysia, Malaysia; Al Ahliyya Amman Univ, Jordan; Middle East Univ, Jordan; Appl Sci Private Univ, Jordan; Univ Sains Malaysia, Malaysia.
    Alameen, Abdalla
    Prince Sattam Bin Abdulaziz Univ, Saudi Arabia.
    Artificial Ecosystem-Based Optimization with Dwarf Mongoose Optimization for Feature Selection and Global Optimization Problems2023Ingår i: International Journal of Computational Intelligence Systems, ISSN 1875-6891, E-ISSN 1875-6883, Vol. 16, nr 1, artikel-id 102Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Meta-Heuristic (MH) algorithms have recently proven successful in a broad range of applications because of their strong capabilities in picking the optimal features and removing redundant and irrelevant features. Artificial Ecosystem-based Optimization (AEO) shows extraordinary ability in the exploration stage and poor exploitation because of its stochastic nature. Dwarf Mongoose Optimization Algorithm (DMOA) is a recent MH algorithm showing a high exploitation capability. This paper proposes AEO-DMOA Feature Selection (FS) by integrating AEO and DMOA to develop an efficient FS algorithm with a better equilibrium between exploration and exploitation. The performance of the AEO-DMOA is investigated on seven datasets from different domains and a collection of twenty-eight global optimization functions, eighteen CEC2017, and ten CEC2019 benchmark functions. Comparative study and statistical analysis demonstrate that AEO-DMOA gives competitive results and is statistically significant compared to other popular MH approaches. The benchmark function results also indicate enhanced performance in high-dimensional search space.

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  • 39.
    Hussien, Abdelazim
    et al.
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt.
    Heidari, Ali Asghar
    Wenzhou Univ, Peoples R China.
    Ye, Xiaojia
    Shanghai Lixin Univ Accounting & Finance, Peoples R China.
    Liang, Guoxi
    Wenzhou Polytech, Peoples R China.
    Chen, Huiling
    Wenzhou Univ, Peoples R China.
    Pan, Zhifang
    Wenzhou Med Univ, Peoples R China.
    Boosting whale optimization with evolution strategy and Gaussian random walks: an image segmentation method2023Ingår i: Engineering with Computers, ISSN 0177-0667, E-ISSN 1435-5663, Vol. 39, s. 1935-1979Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Stochastic optimization has been found in many applications, especially for several local optima problems, because of their ability to explore and exploit various zones of the feature space regardless of their disadvantage of immature convergence and stagnation. Whale optimization algorithm (WOA) is a recent algorithm from the swarm-intelligence family developed in 2016 that attempts to inspire the humpback whale foraging activities. However, the original WOA suffers from getting trapped in the suboptimal regions and slow convergence rate. In this study, we try to overcome these limitations by revisiting the components of the WOA with the evolutionary cores of Gaussian walk, CMA-ES, and evolution strategy that appeared in Virus colony search (VCS). In the proposed algorithm VCSWOA, cores of the VCS are utilized as an exploitation engine, whereas the cores of WOA are devoted to the exploratory phases. To evaluate the resulted framework, 30 benchmark functions from IEEE CEC2017 are used in addition to four different constrained engineering problems. Furthermore, the enhanced variant has been applied in image segmentation, where eight images are utilized, and they are compared with various WOA variants. The comprehensive test and the detailed results show that the new structure has alleviated the central shortcomings of WOA, and we witnessed a significant performance for the proposed VCSWOA compared to other peers.

  • 40.
    Hu, Gang
    et al.
    Xian Univ Technol, Peoples R China.
    Zheng, Yixuan
    Xian Univ Technol, Peoples R China.
    Abualigah, Laith
    Al Al Bayt Univ, Jordan; Yuan Ze Univ, Taiwan; Al Ahliyya Amman Univ, Jordan; Middle East Univ, Jordan; Appl Sci Private Univ, Jordan; Univ Sains Malaysia, Malaysia; Sunway Univ Malaysia, Malaysia.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt.
    DETDO: An adaptive hybrid dandelion optimizer for engineering optimization2023Ingår i: Advanced Engineering Informatics, ISSN 1474-0346, E-ISSN 1873-5320, Vol. 57, artikel-id 102004Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Dandelion Optimizer (DO) is a recently proposed swarm intelligence algorithm that coincides with the process of finding the best reproduction site for dandelion seeds. Compared with the classical Meta-heuristic algorithms, DO exhibits strong competitiveness, but it also has some drawbacks. In this paper, we proposed an adaptive hybrid dandelion optimizer called DETDO by combining three strategies of adaptive tent chaotic mapping, differential evolution (DE) strategy, and adaptive t-distribution perturbation to address the shortcomings of weak DO development, easy to fall into local optimum and slow convergence speed. Firstly, the adaptive tent chaos mapping is used in the initialization phase to obtain a uniformly distributed high-quality initial population, which helps the algorithm to enter the correct search region quickly. Secondly, the DE strategy is introduced to increase the diversity of dandelion populations to avoid algorithm stagnation, which improves the exploitation capability and the accuracy of the optimal solution. Finally, adaptive t-distribution perturbation around the elite solution successfully balances the exploration and exploitation phases while improving the convergence speed through a reasonable conversion from Cauchy to Gaussian distribution. The proposed DETDO is compared with classical or advanced optimization algorithms on CEC2017 and CEC2019 test sets, and the experimental results and statistical analysis demonstrate that the algorithm has better optimization accuracy and speed. In addition, DETDO has obtained the best results in solving six real-world engineering design problems. Finally, DETDO is applied to two bar topology optimization cases. Under a series of complex constraints, DETDO produces a lighter bar structure than the current scheme. It further illustrates the effectiveness and applicability of DETDO in practical problems. The above results mean that DETDO with strong competitiveness will become a preferred swarm intelligence algorithm to cope with optimization problems.

  • 41.
    Hashim, Fatma A.
    et al.
    Helwan Univ, Egypt.
    Neggaz, Nabil
    USTO MB, Algeria.
    Mostafa, Reham R.
    Univ Sharjah, U Arab Emirates; Mansoura Univ, Egypt.
    Abualigah, Laith
    Al al Bayt Univ, Jordan; Lebanese Amer Univ, Lebanon; Al Ahliyya Amman Univ, Jordan; Middle East Univ, Jordan; Appl Sci Private Univ, Jordan; Univ Sains Malaysia, Malaysia; Sunway Univ Malaysia, Malaysia.
    Damasevicius, Robertas
    Silesian Tech Univ, Poland.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt.
    Dimensionality reduction approach based on modified hunger games search: case study on Parkinsons disease phonation2023Ingår i: Neural Computing & Applications, ISSN 0941-0643, E-ISSN 1433-3058, Vol. 35, nr 29, s. 21979-22005Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Hunger Games Search (HGS) is a newly developed swarm-based algorithm inspired by the cooperative behavior of animals and their hunting strategies to find prey. However, HGS has been observed to exhibit slow convergence and may struggle with unbalanced exploration and exploitation phases. To address these issues, this study proposes a modified version of HGS called mHGS, which incorporates five techniques: (1) modified production operator, (2) modified variation control, (3) modified local escaping operator, (4) modified transition factor, and (5) modified foraging behavior. To validate the effectiveness of the mHGS method, 18 different benchmark datasets for dimensionality reduction are utilized, covering a range of sizes (small, medium, and large). Additionally, two Parkinsons disease phonation datasets are employed as real-world applications to demonstrate the superior capabilities of the proposed approach. Experimental and statistical results obtained through the mHGS method indicate its significant performance improvements in terms of Recall, selected attribute count, Precision, F-score, and accuracy when compared to the classical HGS and seven other well-established methods: Gradient-based optimizer (GBO), Grasshopper Optimization Algorithm (GOA), Gray Wolf Optimizer (GWO), Salp Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), Harris Hawks Optimizer (HHO), and Ant Lion Optimizer (ALO).

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  • 42.
    Izci, Davut
    et al.
    Batman Univ, Turkiye; Middle East Univ, Jordan.
    Ekinci, Serdar
    Batman Univ, Turkiye.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt.
    Effective PID controller design using a novel hybrid algorithm for high order systems2023Ingår i: PLOS ONE, E-ISSN 1932-6203, Vol. 18, nr 5, artikel-id e0286060Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper discusses the merging of two optimization algorithms, atom search optimization and particle swarm optimization, to create a hybrid algorithm called hybrid atom search particle swarm optimization (h-ASPSO). Atom search optimization is an algorithm inspired by the movement of atoms in nature, which employs interaction forces and neighbor interaction to guide each atom in the population. On the other hand, particle swarm optimization is a swarm intelligence algorithm that uses a population of particles to search for the optimal solution through a social learning process. The proposed algorithm aims to reach exploration-exploitation balance to improve search efficiency. The efficacy of h-ASPSO has been demonstrated in improving the time-domain performance of two high-order real-world engineering problems: the design of a proportional-integral-derivative controller for an automatic voltage regulator and a doubly fed induction generator-based wind turbine systems. The results show that h-ASPSO outperformed the original atom search optimization in terms of convergence speed and quality of solution and can provide more promising results for different high-order engineering systems without significantly increasing the computational cost. The promise of the proposed method is further demonstrated using other available competitive methods that are utilized for the automatic voltage regulator and a doubly fed induction generator-based wind turbine systems.

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  • 43.
    Hu, Gang
    et al.
    Xian Univ Technol, Peoples R China.
    Wang, Jiao
    Xian Univ Technol, Peoples R China.
    Li, Min
    Xian Univ Technol, Peoples R China.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt.
    Abbas, Muhammad
    Univ Sargodha, Pakistan.
    EJS: Multi-Strategy Enhanced Jellyfish Search Algorithm for Engineering Applications2023Ingår i: Mathematics, E-ISSN 2227-7390, Vol. 11, nr 4, artikel-id 851Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The jellyfish search (JS) algorithm impersonates the foraging behavior of jellyfish in the ocean. It is a newly developed metaheuristic algorithm that solves complex and real-world optimization problems. The global exploration capability and robustness of the JS algorithm are strong, but the JS algorithm still has significant development space for solving complex optimization problems with high dimensions and multiple local optima. Therefore, in this study, an enhanced jellyfish search (EJS) algorithm is developed, and three improvements are made: (i) By adding a sine and cosine learning factors strategy, the jellyfish can learn from both random individuals and the best individual during Type B motion in the swarm to enhance optimization capability and accelerate convergence speed. (ii) By adding a local escape operator, the algorithm can skip the trap of local optimization, and thereby, can enhance the exploitation ability of the JS algorithm. (iii) By applying an opposition-based learning and quasi-opposition learning strategy, the population distribution is increased, strengthened, and more diversified, and better individuals are selected from the present and the new opposition solution to participate in the next iteration, which can enhance the solutions quality, meanwhile, convergence speed is faster and the algorithms precision is increased. In addition, the performance of the developed EJS algorithm was compared with those of the incomplete improved algorithms, and some previously outstanding and advanced methods were evaluated on the CEC2019 test set as well as six examples of real engineering cases. The results demonstrate that the EJS algorithm can skip the trap of local optimization, can enhance the solutions quality, and can increase the calculation speed. In addition, the practical engineering applications of the EJS algorithm also verify its superiority and effectiveness in solving both constrained and unconstrained optimization problems, and therefore, suggests future possible applications for solving such optimization problems.

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  • 44.
    Izci, Davut
    et al.
    Batman Univ, Turkiye; Middle East Univ, Jordan.
    Rizk-Allah, Rizk M.
    Menoufia Univ, Egypt.
    Ekinci, Serdar
    Batman Univ, Turkiye.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt.
    Enhancing time-domain performance of vehicle cruise control system by using a multi-strategy improved RUN optimizer2023Ingår i: Alexandria Engineering Journal, ISSN 1110-0168, E-ISSN 2090-2670, Vol. 80, s. 609-622Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper addresses the pressing concern of traffic safety by focusing on the optimization of vehicle cruise control systems. While traditional control techniques have been widely employed, their design procedures can be time-consuming and suboptimal. To overcome these limitations, metaheuristic algorithms have been introduced as promising solutions for complex optimization problems. In this study, an improved Runge Kutta optimizer (IRUN) is developed and applied to enhance the control performance of a real PID plus second-order derivative (RPIDD2) controller for vehicle cruise control systems. The IRUN optimizer incorporates advanced strategies such as quadratic interpolation, Laplacian segment mutation, Levy flight, and information-sharing-based local search mechanisms. By integrating these strategies, the IRUN algorithm demonstrates enhanced optimization capabil-ities, making it well-suited for tuning the controller. The proposed approach utilizes a master-slave system, where the ideal reference model sets the desired response and the RPIDD2 controller adjusts its parameters accordingly. The integral of the square error is employed as the objective function to evaluate the control sys-tems performance. Statistical analyses, convergence analyses, and stability evaluations and robustness analysis are performed to demonstrate the effectiveness of the IRUN-based RPIDD2 controller. Comparative studies are conducted against established approaches using PID, fractional-order PID (FOPID), and RPIDD2 controllers, showcasing the superiority and effectiveness of the proposed approach. Overall, this paper presents a compre-hensive study on enhancing the time-domain performance and stability of vehicle cruise control systems, providing significant improvements in control accuracy and efficiency. The subsequent sections delve into the proposed approach, experimental setup, and obtained results, further emphasizing the significance and potential impact of this research.

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  • 45.
    Mevada, Dinesh
    et al.
    Govt Engn Coll, India.
    Panchal, Hitesh
    Govt Engn Coll, India.
    Nayyar, Anand
    Duy Tan Univ, Vietnam.
    Sharma, Kamal
    GLA Univ, India.
    Manokar, A. Muthu
    BS Abdur Rahman Crescent Inst Sci & Technol, India.
    El-Sebaey, Mahmoud S.
    Menoufia Univ, Egypt.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt; Middle East Univ, Jordan; Middle East Univ, Jordan.
    Experimental performance evaluation of solar still with zig-zag shape air cooled condenser: An energy-exergy analysis approach2023Ingår i: Energy Reports, E-ISSN 2352-4847, Vol. 10, s. 1198-1210Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In the present experimental effort is made to increase the performance of a solar still (SS) by including a novel design of a zig-zag-shaped air-cooled condenser (ZZACC) and cuprous oxide (CuO) as a nanomaterial. Research work is conducted in the climatic conditions of Gandhinagar, Gujarat, India, from September to November 2020. A comparison was made to assess the performance of a conventional solar still (CSS) and a solar still equipped with a zig-zag shape air-cooled condenser (SSWZZACC) with CuO. The experiments findings showed that adding CuO to SSWZZACC increases the distillate production by 46.83% and the daily energy efficiency by 45.98%, respectively, compared to CSS. Also, SSWZZACC demonstrates a better efficiency of exergy and latent heat of vaporization than CSS because CuO causes an increase in the evaporative heat transfer coefficient of water. In life cycle cost analysis study discovered that SSWZZACC has a 27.77% lower cost per litre of water (CPL) than CSS. The obtained maximum energy and exergy efficiency values for CSS and SSWZZACC were 2.36% & 25.75% and 3.9% & 37.59%, respectively. In economic and environmental aspects, it was found that SSWZZACC with CuO showed a cost-effective desalination unit and was highly effective from a carbon credit point of view (CCP) by CO2 mitigation.& COPY; 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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  • 46.
    Hashim, Fatma A.
    et al.
    Helwan Univ, Egypt.
    Mostafa, Reham R.
    Mansoura Univ, Egypt.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt.
    Mirjalili, Seyedali
    King Abdulaziz Univ, Saudi Arabia; Torrens Univ Australia, Australia; Yonsei Univ, South Korea.
    Sallam, Karam M.
    Univ Canberra, Australia.
    Fick’s Law Algorithm: A physical law-based algorithm for numerical optimization2023Ingår i: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 260, artikel-id 110146Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Recently, many metaheuristic optimization algorithms have been developed to address real-world issues. In this study, a new physics-based metaheuristic called Ficks law optimization (FLA) is presented, in which Ficks first rule of diffusion is utilized. According to Ficks law of diffusion, molecules tend to diffuse from higher to lower concentration areas. Many experimental series are done to test FLAs performance and ability in solving different optimization problems. Firstly, FLA is tested using twenty well-known benchmark functions and thirty CEC2017 test functions. Secondly, five real-world engineering problems are utilized to demonstrate the feasibility of the proposed FLA. The findings are compared with 12 well-known and powerful optimizers. A Wilcoxon rank-sum test is carried out to evaluate the comparable statistical performance of competing algorithms. Results prove that FLA achieves competitive and promising findings, a good convergence curve rate, and a good balance between exploration and exploitation. The source code is currently available for public from: https://se.mathworks.com/matlabcentral/fileexchange/121033-fick-s-law-algorithm-fla.(c) 2022 Elsevier B.V. All rights reserved.

  • 47.
    Chhabra, Amit
    et al.
    Guru Nanak Dev Univ, India.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt.
    Hashim, Fatma A.
    Helwan Univ, Egypt.
    Improved bald eagle search algorithm for global optimization and feature selection2023Ingår i: Alexandria Engineering Journal, ISSN 1110-0168, E-ISSN 2090-2670, Vol. 68, s. 141-180Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The use of metaheuristics is one of the most encouraging methodologies for taking care of real-life problems. Bald eagle search (BES) algorithm is the latest swarm-intelligence metaheuris-tic algorithm inspired by the intelligent hunting behavior of bald eagles. In recent research works, BES algorithm has performed reasonably well over a wide range of application areas such as chem-ical engineering, environmental science, physics and astronomy, structural modeling, global opti-mization, engineering design, energy efficiency, etc. However, it still lacks adequate searching efficiency and has a tendency to stuck in local optima which affects the final outcome. This paper introduces a modified BES (mBES) algorithm that removes the shortcomings of the original BES algorithm by incorporating three improvements; Opposition-based learning (OBL), Chaotic Local Search (CLS), and Transition & Pharsor operators. OBL is embedded in different phases of the standard BES viz. initial population, selecting, searching in space, and swooping phases to update the positions of individual solutions to strengthen exploration, CLS is used to enhance the position of the best agent which will lead to enhancing the positions of all individuals, and Transition & Pharsor operators help to provide sufficient exploration-exploitation trade-off. The efficiency of the mBES algorithm is initially evaluated with 29 CEC2017 and 10 CEC2020 global optimization benchmark functions. In addition, the practicality of the mBES is tested with a real-world feature selection problem and five engineering design problems. Results of the mBES algorithm are com-pared against a number of classical metaheuristic algorithms using statistical metrics, convergence analysis, box plots, and the Wilcoxon rank sum test. In the case of composite CEC2017 test func-tions F21-F30, mBES wins against compared algorithms in 70% test cases, whereas for the rest of the test functions, it generates good results in 65% cases. The proposed mBES produces best per-formance in 55% of the CEC2020 test functions, whereas for the rest of the 45% test cases, it gen-erated competitive results. On the other hand, for five engineering design problems, the mBES is the best among all compared algorithms. In the case of the feature selection problem, the mBES also showed competitiveness with the compared algorithms. Results and observations for all tested opti-mization problems show the superiority and robustness of the proposed mBES over the baseline metaheuristics. It can be safely concluded that the improvements suggested in the mBES are proved to be effective making it competitive enough to solve a variety of optimization problems.(c) 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

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  • 48.
    Abualigah, Laith
    et al.
    Al al Bayt Univ, Jordan; Al Ahliyya Amman Univ, Jordan; Lebanese Amer Univ, Lebanon; Middle East Univ, Jordan; Appl Sci Private Univ, Jordan; Univ Sains Malaysia, Malaysia; Sunway Univ Malaysia, Malaysia.
    Oliva, Diego
    Univ Guadalajara, Mexico.
    Jia, Heming
    Sanming Univ, Peoples R China.
    Gul, Faiza
    Air Univ, Pakistan.
    Khodadadi, Nima
    Florida Int Univ, FL USA.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt.
    Al Shinwan, Mohammad
    Appl Sci Private Univ, Jordan.
    Ezugwu, Absalom E.
    North West Univ, South Africa.
    Abuhaija, Belal
    Wenzhou Kean Univ, Peoples R China.
    Abu Zitar, Raed
    Sorbonne Univ Abu Dhabi, U Arab Emirates.
    Improved prairie dog optimization algorithm by dwarf mongoose optimization algorithm for optimization problems2023Ingår i: Multimedia tools and applications, ISSN 1380-7501, E-ISSN 1573-7721, Vol. 83, nr 11, s. 32613-32653Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Recently, optimization problems have been revised in many domains, and they need powerful search methods to address them. In this paper, a novel hybrid optimization algorithm is proposed to solve various benchmark functions, which is called IPDOA. The proposed method is based on enhancing the search process of the Prairie Dog Optimization Algorithm (PDOA) by using the primary updating mechanism of the Dwarf Mongoose Optimization Algorithm (DMOA). The main aim of the proposed IPDOA is to avoid the main weaknesses of the original methods; these weaknesses are poor convergence ability, the imbalance between the search process, and premature convergence. Experiments are conducted on 23 standard benchmark functions, and the results are compared with similar methods from the literature. The results are recorded in terms of the best, worst, and average fitness function, showing that the proposed method is more vital to deal with various problems than other methods.

  • 49.
    Mir, Imran
    et al.
    NUST, Pakistan.
    Gul, Faiza
    Air Univ, Pakistan.
    Mir, Suleman
    Natl Univ Comp & Emerging Sci, Pakistan.
    Abualigah, Laith
    Al Al Bayt Univ, Jordan; Al Ahliyya Amman Univ, Jordan; Middle East Univ, Jordan; Appl Sci Private Univ, Jordan.
    Abu Zitar, Raed
    Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, Abu Dhabi, United Arab Emirates.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten.
    Awwad, Emad Mahrous
    King Saud Univ, Saudi Arabia.
    Sharaf, Mohamed
    King Saud Univ, Saudi Arabia.
    Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective2023Ingår i: BIOMIMETICS, ISSN 2313-7673, Vol. 8, nr 3, artikel-id 294Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This study proposes an adaptable, bio-inspired optimization algorithm for Multi-Agent Space Exploration. The recommended approach combines a parameterized Aquila Optimizer, a bio-inspired technology, with deterministic Multi-Agent Exploration. Stochastic factors are integrated into the Aquila Optimizer to enhance the algorithms efficiency. The architecture, called the Multi-Agent Exploration-Parameterized Aquila Optimizer (MAE-PAO), starts by using deterministic MAE to assess the cost and utility values of nearby cells encircling the agents. A parameterized Aquila Optimizer is then used to further increase the exploration pace. The effectiveness of the proposed MAE-PAO methodology is verified through extended simulations in various environmental conditions. The algorithm viability is further evaluated by comparing the results with those of the contemporary CME-Aquila Optimizer (CME-AO) and the Whale Optimizer. The comparison adequately considers various performance parameters, such as the percentage of the map explored, the number of unsuccessful runs, and the time needed to explore the map. The comparisons are performed on numerous maps simulating different scenarios. A detailed statistical analysis is performed to check the efficacy of the algorithm. We conclude that the proposed algorithms average rate of exploration does not deviate much compared to contemporary algorithms. The same idea is checked for exploration time. Thus, we conclude that the results obtained for the proposed MAE-PAO algorithm provide significant advantages in terms of enhanced map exploration with lower execution times and nearly no failed runs.

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  • 50.
    Hashim, Fatma A.
    et al.
    Helwan Univ, Egypt; Middle East Univ, Jordan.
    Abu Khurma, Ruba
    Al Ahliyya Univ, Jordan.
    Albashish, Dheeb
    Al Balqa Appl Univ, Jordan.
    Amin, Mohamed
    Menoufia Univ, Egypt.
    Hussien, Abdelazim
    Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. Fayoum Univ, Egypt.
    Novel hybrid of AOA-BSA with double adaptive and random spare for global optimization and engineering problems2023Ingår i: Alexandria Engineering Journal, ISSN 1110-0168, E-ISSN 2090-2670, Vol. 73, s. 543-577Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Archimedes Optimization Algorithm (AOA) is a new physics-based optimizer that sim-ulates Archimedes principles. AOA has been used in a variety of real-world applications because of potential properties such as a limited number of control parameters, adaptability, and changing the set of solutions to prevent being trapped in local optima. Despite the wide acceptance of AOA, it has some drawbacks, such as the assumption that individuals modify their locations depending on altered densities, volumes, and accelerations. This causes various shortcomings such as stagnation into local optimal regions, low diversity of the population, weakness of exploitation phase, and slow convergence curve. Thus, the exploitation of a specific local region in the conventional AOA may be examined to achieve a balance between exploitation and exploration capabilities in the AOA. The bird Swarm Algorithm (BSA) has an efficient exploitation strategy and a strong ability of search process. In this study, a hybrid optimizer called AOA-BSA is proposed to overcome the limitations of AOA by replacing its exploitation phase with a BSA exploitation one. Moreover, a transition operator is used to have a high balance between exploration and exploitation. To test and examine the AOA-BSA performance, in the first experimental series, 29 unconstrained functions from CEC2017 have been used whereas the series of the second experiments use seven constrained engi-neering problems to test the AOA-BSAs ability in handling unconstrained issues. The performance of the suggested algorithm is compared with 10 optimizers. These are the original algorithms and 8 other algorithms. The first experiments results show the effectiveness of the AOA-BSA in optimiz-ing the functions of the CEC2017 test suite. AOABSA outperforms the other metaheuristic algo-rithms compared with it across 16 functions. The results of AOABSA are statically validated using Wilcoxon Rank sum. The AOABSA shows superior convergence capability. This is due to the added power to the AOA by the integration with BSA to balance exploration and exploitation. This is not only seen in the faster convergence achieved by the AOABSA, but also in the optimal solutions found by the search process. For further validation of the AOABSA, an extensive statis-tical analysis is performed during the search process by recording the ratios of the exploration and exploitation. For engineering problems, AOABSA achieves competitive results compared with other algorithms. the convergence curve of the AOABSA reaches the lowest values of the problem. It also has the minimum standard deviation which indicates the robustness of the algorithm in solv-ing these problems. Also, it obtained competitive results compared with other counterparts algo-rithms regarding the values of the problem variables and convergence behavior that reaches the best minimum values. (c) 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

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