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  • 1.
    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
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. 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 problems2023In: Multimedia tools and applications, ISSN 1380-7501, E-ISSN 1573-7721Article in journal (Refereed)
    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.

  • 2.
    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
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. 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 heaters2024In: Energy Reports, E-ISSN 2352-4847, Vol. 11, p. 963-981Article in journal (Refereed)
    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.

  • 3.
    Alhenawi, Esra'a
    et al.
    Zarqa Univ, Jordan.
    Abu Khurma, Ruba
    Middle East Univ, Jordan.
    Damasevicius, Robertas
    Vytautas Magnus Univ, Lithuania.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. 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 Spark2024In: International Journal of Computational Intelligence Systems, ISSN 1875-6891, E-ISSN 1875-6883, Vol. 17, no 1, article id 4Article in journal (Refereed)
    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.

  • 4.
    Alshourbaji, Ibrahim
    et al.
    Univ Hertfordshire, England; Jazan Univ, Saudi Arabia.
    Helian, Na
    Univ Hertfordshire, England.
    Sun, Yi
    Univ Hertfordshire, England.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. 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 optimization2023In: Scientific Reports, E-ISSN 2045-2322, Vol. 13, no 1, article id 14441Article in journal (Refereed)
    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.

  • 5.
    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
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. 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 Problems2023In: International Journal of Computational Intelligence Systems, ISSN 1875-6891, E-ISSN 1875-6883, Vol. 16, no 1, article id 102Article in journal (Refereed)
    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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  • 6.
    Chhabra, Amit
    et al.
    Guru Nanak Dev Univ, India.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt.
    Hashim, Fatma A.
    Helwan Univ, Egypt.
    Improved bald eagle search algorithm for global optimization and feature selection2023In: Alexandria Engineering Journal, ISSN 1110-0168, E-ISSN 2090-2670, Vol. 68, p. 141-180Article in journal (Refereed)
    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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  • 7.
    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
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. 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 systems2024In: Energy Reports, E-ISSN 2352-4847, Vol. 11, p. 982-1005Article in journal (Refereed)
    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.

  • 8.
    Ekinci, Serdar
    et al.
    Batman Univ, Turkiye.
    Izci, Davut
    Batman Univ, Turkiye; Middle East Univ, Jordan.
    Abualigah, Laith
    Ho Chi Minh City Open Univ, Vietnam; Al Ahliyya Amman Univ, Jordan; Lebanese Amer Univ, Lebanon; Appl Sci Private Univ, Jordan.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt.
    Thanh, Cuong-Le
    Ho Chi Minh City Open Univ, Vietnam.
    Khatir, Samir
    Ho Chi Minh City Open Univ, Vietnam; Univ Ghent, Belgium.
    Revolutionizing Vehicle Cruise Control: An Elite Opposition-Based Pattern Search Mechanism Augmented INFO Algorithm for Enhanced Controller Design2023In: International Journal of Computational Intelligence Systems, ISSN 1875-6891, E-ISSN 1875-6883, Vol. 16, no 1, article id 129Article in journal (Refereed)
    Abstract [en]

    This paper presents a groundbreaking approach to enhance the performance of a vehicle cruise control system-a crucial aspect of road safety. The work offers two key contributions. Firstly, a state-of-the-art metaheuristic algorithm is proposed by augmenting the performance of the weighted mean of vectors (INFO) algorithm using pattern search and elite opposition-based learning mechanisms. The resulting boosted INFO (b-INFO) algorithm surpasses the original INFO, marine predators, and gravitational search algorithms in terms of performance on benchmark functions, including unimodal, multimodal, and fixed-dimensional multimodal functions. Secondly, a novel proportional, fractional order integral, derivative plus double derivative with filter ((PIDNDN2)-D-?-N-2) controller is proposed as a more efficient control structure for vehicle cruise control systems. An objective function is utilized to determine the optimal values for the controller parameters, and the proposed methods performance is compared against a range of recent approaches. Results demonstrate that the b-INFO algorithm-based (PIDNDN2)-D-?-N-2 controller is the most efficient and superior method for controlling a vehicle cruise control system. Moreo-ver, this work represents the first report of a (PIDNDN2)-D-?-N-2 controllers implementation for vehicle cruise control systems, underscoring the novelty and significance of this research. The proposed methods exceptional ability is further confirmed by comparisons with the genetic algorithm, ant lion optimizer, atom search optimizer, arithmetic optimization algorithm, slime mold algorithm, L & eacute;vy flight distribution algorithm, manta ray foraging optimization, and hunger games search-based proportional-integral-derivative (PID), along with Harris hawks optimization-based PID and fractional order PID control-lers. This work marks a remarkable milestone toward safer and more efficient vehicle cruise control systems.

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  • 9.
    Elseify, Mohamed A.
    et al.
    Al Azhar Univ, Egypt.
    Hashim, Fatma A.
    Helwan Univ, Egypt.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. 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 systems2024In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 353, article id 122054Article in journal (Refereed)
    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

  • 10.
    Fathi, Hanaa
    et al.
    Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Al Minufya, Egypt.
    AlSalman, Hussain
    Department of Computer Science, College of Computer and Information Sciences, King Saud University.
    Gumaei, Abdu
    Computer Science Department, Faculty of Applied Science, Taiz University, Taiz, Yemen.
    Manhrawy, Ibrahim I. M.
    Department of Basic Science, Modern Academy, Cairo, Egypt.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Faculty of Science, Fayoum University, Faiyum, Egypt.
    El-Kafrawy, Passent
    Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Al Minufya, Egypt; School of Information Technology and Computer Science, Nile University, Giza, Egypt.
    An Efficient Cancer Classification Model Using Microarray and High-Dimensional Data2021In: Computational Intelligence and Neuroscience, ISSN 1687-5265, E-ISSN 1687-5273, Vol. 2021, article id 7231126Article in journal (Refereed)
    Abstract [en]

    Cancer can be considered as one of the leading causes of death widely. One of the most effective tools to be able to handle cancer diagnosis, prognosis, and treatment is by using expression profiling technique which is based on microarray gene. For each data point (sample), gene data expression usually receives tens of thousands of genes. As a result, this data is large-scale, high-dimensional, and highly redundant. The classification of gene expression profiles is considered to be a (NP)-Hard problem. Feature (gene) selection is one of the most effective methods to handle this problem. A hybrid cancer classification approach is presented in this paper, and several machine learning techniques were used in the hybrid model: Pearsons correlation coefficient as a correlation-based feature selector and reducer, a Decision Tree classifier that is easy to interpret and does not require a parameter, and Grid Search CV (cross-validation) to optimize the maximum depth hyperparameter. Seven standard microarray cancer datasets are used to evaluate our model. To identify which features are the most informative and relative using the proposed model, various performance measurements are employed, including classification accuracy, specificity, sensitivity, F1-score, and AUC. The suggested strategy greatly decreases the number of genes required for classification, selects the most informative features, and increases classification accuracy, according to the results.

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  • 11.
    Gubin, Pavel Y.
    et al.
    Ural Fed Univ, Russia; Sci & Engn Ctr Reliabil & Safety Large Syst & Mach, Russia.
    Kamel, Salah
    Aswan Univ, Egypt.
    Safaraliev, Murodbek
    Ural Fed Univ, Russia.
    Senyuk, Mihail
    Ural Fed Univ, Russia.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt; Middle East Univ, Jordan.
    Zawbaa, Hossam M.
    Beni Suef Univ, Egypt; Appl Sci Private Univ, Jordan.
    Optimizing generating unit maintenance with the league championship method: A reliability-based approach2023In: Energy Reports, E-ISSN 2352-4847, Vol. 10, p. 135-152Article in journal (Refereed)
    Abstract [en]

    The electrical power industry has experienced an unprecedented pace of digital transformation as a prevailing economic trend in recent years. This shift towards digitalization has resulted in an increasing interest in the collection of real-time equipment condition data, which provides opportunities for implementing sensor-driven condition-based repair. As a result, there is a growing need for the development of generator maintenance scheduling to consider probabilistic equipment behavior, which requires significant computational efforts. To address this issue, the research proposes the use of a meta-heuristic league championship method (LCM) for generator maintenance scheduling, considering random generation profiles based on generation adequacy criteria. The experimental part of the study compares this approach and its modifications to widely used meta-heuristics, such as differential evolution and particle swarm methods. The identification and demonstration of optimal method settings for the generation maintenance scheduling problem are presented. Subsequently, it is illustrated that employing random league scheduling expedience can reduce the variance of objective function values in resulting plans by over three times, with values of 0.632 MWh and 0.205 MWh for conventional and proposed techniques respectively. In addition, three approaches are compared to assess generation adequacy corresponding to different schedules. The study emphasizes the efficacy of employing the LCM approach in scheduling generator maintenance. Specifically, it showcases that among all the methods examined, the LCM approach exhibits the lowest variance in objective function values, with values of 38.81 and 39.90 MWh for LCM and its closest rival, the modified particle swarm method (MPSM), respectively.& COPY; 2023 The Author(s). Published by Elsevier Ltd. 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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  • 12.
    Habib, Salman
    et al.
    Hohai Univ, Peoples R China; Southern Univ Sci & Technol, Peoples R China.
    Liu, Haoming
    Hohai Univ, Peoples R China.
    Tamoor, Muhammad
    Govt Coll Univ Faisalabad, Pakistan.
    Zaka, Muhammad Ans
    Zeecon Engn Serv, Pakistan.
    Jia, Youwei
    Southern Univ Sci & Technol, Peoples R China.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt; Middle East Univ, Jordan.
    Zawbaa, Hossam M.
    Beni Suef Univ, Egypt; Appl Sci Private Univ, Jordan.
    Kamel, Salah
    Aswan Univ, Egypt.
    Technical modelling of solar photovoltaic water pumping system and evaluation of system performance and their socio-economic impact2023In: Heliyon, E-ISSN 2405-8440, Vol. 9, no 5, article id e16105Article in journal (Refereed)
    Abstract [en]

    Water is a precious resource for agriculture and most of the land is irrigated by tube wells. Diesel engines and electricity-operated pumps are widely used to fulfill irrigation water requirements; such conventional systems are inefficient and costly. With rising concerns about global warming, it is important to choose renewable energy source. In this study, SPVWPS has been optimally designed considering the water requirement, solar resources, tilt angle and orientation, losses in both systems and performance ratio. A PVSyst and SoSiT simulation tools were used to perform simulation analysis of the designed solar photovoltaic WPS. After designing and performance analysis, farmers were interviewed during fieldwork to assess socioeconomic impacts. In the result section, performance of PV system is analyzed at various tilt angles and it is established that system installed at a 15 degrees tilt angle is more efficient. The annual PV array virtual energy at MPP of designed photovoltaic system is 33342 kWh and the annual energy available to operate the WPS is 23502 kWh. Module array mismatch and ohmic wiring losses are 374.16 kWh and 298.83 kWh, respectively. The total annual water demand of the selected site is 80769 m3 and designed SPWPS pumped 75054 m3 of water, supplying 92.93% of the irrigation demand. The normalized values of the effective energy, system losses, collection losses and unused energy in the SPVWP system are 2.6 kW/kWp/day, 0.69 kW/kWp/day, 0.72 kW/kWp/day and 0.48 kW/kWp/day, respec-tively. The annual average performance ratio of the proposed system is 74.62%. The results of the interviews showed that 70% of farmers are extremely satisfied with the performance of SPVWPS and 84% of farmers indicated that they did not incur any operating costs. The unit cost of the SPWPS is 0.17 euro/kWh, which is 56.41% and 19.04% less expensive than the cost of diesel and grid electricity.

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  • 13.
    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
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt.
    Novel hybrid of AOA-BSA with double adaptive and random spare for global optimization and engineering problems2023In: Alexandria Engineering Journal, ISSN 1110-0168, E-ISSN 2090-2670, Vol. 73, p. 543-577Article in journal (Refereed)
    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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  • 14.
    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
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt.
    Helmy, Fatma
    Misr Int Univ, Egypt.
    An efficient adaptive-mutated Coati optimization algorithm for feature selection and global optimization2023In: Alexandria Engineering Journal, ISSN 1110-0168, E-ISSN 2090-2670, Vol. 85, p. 29-48Article in journal (Refereed)
    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.

  • 15.
    Hashim, Fatma A.
    et al.
    Helwan Univ, Egypt.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt.
    Snake Optimizer: A novel meta-heuristic optimization algorithm2022In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 242, article id 108320Article in journal (Refereed)
    Abstract [en]

    In recent years, several metaheuristic algorithms have been introduced in engineering and scientific fields to address real-life optimization problems. In this study, a novel nature-inspired metaheuristics algorithm named as Snake Optimizer (SO) is proposed to tackle a various set of optimization tasks which imitates the special mating behavior of snakes. Each snake (male/female) fights to have the best partner if the existed quantity of food is enough and the temperature is low. This study mathematically mimics and models such foraging and reproduction behaviors and patterns to present a simple and efficient optimization algorithm. To verify the validity and superiority of the proposed method, SO is tested on 29 unconstrained Congress on Evolutionary Computation (CEC) 2017 benchmark functions and four constrained real-world engineering problems. SO is compared with other 9 well-known and newly developed algorithms such as Linear population size reduction-Success-History Adaptation for Differential Evolution (L-SHADE), Ensemble Sinusoidal incorporated with L-SHADE (LSHADE-EpSin), Covariance matrix adaptation evolution strategy (CMAES), Coyote Optimization Algorithm (COA), Moth-flame Optimization, Harris Hawks Optimizer, Thermal Exchange optimization, Grasshopper Optimization Algorithm, and Whale Optimization Algorithm. Experimental results and statistical comparisons prove the effectiveness and efficiency of SO on different landscapes with respect to exploration-exploitation balance and convergence curve speed.

  • 16.
    Hashim, Fatma A.
    et al.
    Helwan Univ, Egypt.
    Mostafa, Reham R.
    Mansoura Univ, Egypt.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. 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 optimization2023In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 260, article id 110146Article in journal (Refereed)
    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.

  • 17.
    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
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt.
    Dimensionality reduction approach based on modified hunger games search: case study on Parkinsons disease phonation2023In: Neural Computing & Applications, ISSN 0941-0643, E-ISSN 1433-3058, Vol. 35, no 29, p. 21979-22005Article in journal (Refereed)
    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).

  • 18.
    Hassan, Mohamed H.
    et al.
    Minist Elect & Renewable Energy, Egypt.
    Daqaq, Fatima
    Mohammed V Univ Rabat, Morocco.
    Kamel, Salah
    Aswan Univ, Egypt.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. 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 integration2023In: IET Generation, Transmission & Distribution, ISSN 1751-8687, E-ISSN 1751-8695, Vol. 17, no 14, p. 3115-3139Article in journal (Refereed)
    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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  • 19.
    Hassan, Mohamed H.
    et al.
    Minist Elect & Renewable Energy, Egypt.
    Kamel, Salah
    Aswan Univ, Egypt.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt; Middle East Univ, Jordan.
    Optimal power flow analysis considering renewable energy resources uncertainty based on an improved wild horse optimizer2023In: IET Generation, Transmission & Distribution, ISSN 1751-8687, E-ISSN 1751-8695, Vol. 17, no 16, p. 3582-3606Article in journal (Refereed)
    Abstract [en]

    In recent years, electricity networks across the globe have undergone rapid development, especially with the incorporation of various renewable energy sources (RES). The goal is to increase the penetration level of RES in the power grid to maximize energy efficiency. However, the optimal power flow (OPF) problem for conventional power generation with RES integration is highly complex, non-linear, and non-convex, and this complexity is further compounded when stochastic RES is integrated into the network. To address this problem, this article proposes an elite evolutionary strategy (EES) based on evolutionary approaches to improve the Wild Horse Optimizer (WHO), creating an enhanced hybrid technique called EESWHO. The proposed techniques effectiveness and robustness were tested on 23 numerical optimization test functions, including seven unimodal, six multimodal, and ten composite test functions. Furthermore, the EESWHO was applied to the modified IEEE-30 bus test system to demonstrate its supremacy and efficacy in achieving the optimal solution. The simulation results show that the proposed EESWHO algorithm is highly effective and robust in achieving the optimal solution to the OPF problem with stochastic RES. This approach provides a practical solution to the challenges posed by the integration of RES into power networks, allowing for maximum energy efficiency while minimizing system complexity.

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  • 20.
    Hassan, Mohamed H.
    et al.
    Minist Elect & Renewable Energy, Egypt.
    Kamel, Salah
    Aswan Univ, Egypt.
    Shaikh, Muhammad Suhail
    Hanshan Normal Univ, Peoples R China.
    Alquthami, Thamer
    King Abdulaziz Univ, Saudi Arabia.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt; Middle East Univ, Jordan.
    Supply-demand optimizer for economic emission dispatch incorporating price penalty factor and variable load demand levels2023In: IET Generation, Transmission & Distribution, ISSN 1751-8687, E-ISSN 1751-8695, Vol. 17, no 14, p. 3211-3231Article in journal (Refereed)
    Abstract [en]

    The Economic and Emission Dispatch (EED) method is widely used to optimize generator output in a power system. The goal is to reduce fuel costs and emissions, including carbon dioxide, sulphur dioxide, and nitrogen oxides, while maintaining power balance and adhering to limit constraints. EED aims to minimize emissions and operating costs while meeting power demands. To solve the multi-objective EED problem, the supply-demand optimization (SDO) algorithm is proposed, which employs a price penalty factor approach to convert it into a single-objective function. The SDO algorithm uses a swarm-based optimization strategy inspired by supply-demand mechanisms in economics. The algorithms performance is evaluated on seven benchmark functions before being used to simulate the EED problem on power systems with varying numbers of units and load demands. Established algorithms like the Grey Wolf Optimizer (GWO), Moth-Flame Optimization (MFO), Transient Search Optimization (TSO), and Whale Optimization Algorithm (WOA) are compared to the SDO algorithm. The simulations are conducted on power systems with different numbers of units and load demands to optimize power generation output. The numerical analyses demonstrate that the SDO technique is more efficient and produces higher quality solutions than other recent optimization methods.

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  • 21.
    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
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt.
    Abbas, Muhammad
    Univ Sargodha, Pakistan.
    EJS: Multi-Strategy Enhanced Jellyfish Search Algorithm for Engineering Applications2023In: Mathematics, E-ISSN 2227-7390, Vol. 11, no 4, article id 851Article in journal (Refereed)
    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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  • 22.
    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
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt.
    DETDO: An adaptive hybrid dandelion optimizer for engineering optimization2023In: Advanced Engineering Informatics, ISSN 1474-0346, E-ISSN 1873-5320, Vol. 57, article id 102004Article in journal (Refereed)
    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.

  • 23.
    Hussien, Abdelazim
    et al.
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. 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 optimizer2024In: Journal of Energy Storage, ISSN 2352-152X, E-ISSN 2352-1538, Vol. 78, article id 109974Article in journal (Refereed)
    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).

  • 24.
    Hussien, Abdelazim
    et al.
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt.
    Abu Khurma, Ruba
    Al Ahliyya Univ, Jordan.
    Alzaqebah, Abdullah
    World Islamic Sci & Educ Univ, Jordan.
    Amin, Mohamed
    Menoufia Univ, Egypt.
    Hashim, Fatma A.
    Helwan Univ, Egypt; Middle East Univ, Jordan.
    Novel memetic of beluga whale optimization with self-adaptive exploration-exploitation balance for global optimization and engineering problems2023In: Soft Computing - A Fusion of Foundations, Methodologies and Applications, ISSN 1432-7643, E-ISSN 1433-7479Article in journal (Refereed)
    Abstract [en]

    A population-based optimizer called beluga whale optimization (BWO) depicts behavioral patterns of water aerobics, foraging, and diving whales. BWO runs effectively, nevertheless it retains numerous of deficiencies that has to be strengthened. Premature convergence and a disparity between exploitation and exploration are some of these challenges. Furthermore, the absence of a transfer parameter in the typical BWO when moving from the exploration phase to the exploitation phase has a direct impact on the algorithms performance. This work proposes a novel modified BWO (mBWO) optimizer that incorporates an elite evolution strategy, a randomization control factor, and a transition factor between exploitation and exploitation. The elite strategy preserves the top candidates for the subsequent generation so it helps generate effective solutions with meaningful differences between them to prevent settling into local maxima. The elite random mutation improves the search strategy and offers a more crucial exploration ability that prevents stagnation in the local optimum. The mBWO incorporates a controlling factor to direct the algorithm away from the local optima region during the randomization phase of the BWO. Gaussian local mutation (GM) acts on the initial position vector to produce a new location. Because of this, the majority of altered operators are scattered close to the original position, which is comparable to carrying out a local search in a small region. The original method can now depart the local optimal zone because to this modification, which also increases the optimizers optimization precision control randomization traverses the search space using random placements, which can lead to stagnation in the local optimal zone. Transition factor (TF) phase are used to make the transitions of the agents from exploration to exploitation gradually concerning the amount of time required. The mBWO undergoes comparison to the original BWO and 10 additional optimizers using 29 CEC2017 functions. Eight engineering problems are addressed by mBWO, involving the design of welded beams, three-bar trusses, tension/compression springs, speed reducers, the best design of industrial refrigeration systems, pressure vessel design challenges, cantilever beam designs, and multi-product batch plants. In both constrained and unconstrained settings, the results of mBWO preformed superior to those of other methods.

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  • 25.
    Hussien, Abdelazim
    et al.
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt.
    Abualigah, Laith
    Amman Arab Univ, Jordan.
    Abu Zitar, Raed
    Sorbonne Univ Abu Dhabi, U Arab Emirates.
    Hashim, Fatma A.
    Helwan Univ, Egypt.
    Amin, Mohamed
    Menoufia Univ, Egypt.
    Saber, Abeer
    Kafr El Sheikh Univ, Egypt.
    Almotairi, Khaled H.
    Umm Al Qura Univ, Saudi Arabia.
    Gandomi, Amir H.
    Univ Technol Sydney, Australia.
    Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications2022In: Electronics, E-ISSN 2079-9292, Vol. 11, no 12, article id 1919Article, review/survey (Refereed)
    Abstract [en]

    The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.

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  • 26.
    Hussien, Abdelazim
    et al.
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt.
    Hashim, Fatma A.
    Helwan Univ, Egypt.
    Qaddoura, Raneem
    Al Hussein Tech Univ, Jordan.
    Abualigah, Laith
    Al Ahliyya Amman Univ, Jordan; Middle East Univ, Jordan; Univ Sains Malaysia, Malaysia.
    Pop, Adrian
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
    An Enhanced Evaporation Rate Water-Cycle Algorithm for Global Optimization2022In: Processes, ISSN 2227-9717, Vol. 10, no 11, article id 2254Article in journal (Refereed)
    Abstract [en]

    Water-cycle algorithm based on evaporation rate (ErWCA) is a powerful enhanced version of the water-cycle algorithm (WCA) metaheuristics algorithm. ErWCA, like other algorithms, may still fall in the sub-optimal region and have a slow convergence, especially in high-dimensional tasks problems. This paper suggests an enhanced ErWCA (EErWCA) version, which embeds local escaping operator (LEO) as an internal operator in the updating process. ErWCA also uses a control-randomization operator. To verify this version, a comparison between EErWCA and other algorithms, namely, classical ErWCA, water cycle algorithm (WCA), butterfly optimization algorithm (BOA), bird swarm algorithm (BSA), crow search algorithm (CSA), grasshopper optimization algorithm (GOA), Harris Hawks Optimization (HHO), whale optimization algorithm (WOA), dandelion optimizer (DO) and fire hawks optimization (FHO) using IEEE CEC 2017, was performed. The experimental and analytical results show the adequate performance of the proposed algorithm.

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  • 27.
    Hussien, Abdelazim
    et al.
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. 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 method2023In: Engineering with Computers, ISSN 0177-0667, E-ISSN 1435-5663, Vol. 39, p. 1935-1979Article in journal (Refereed)
    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.

  • 28.
    Hussien, Abdelazim
    et al.
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. 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 selection2023In: JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING, ISSN 2288-5048, Vol. 11, no 1, p. 49-72Article in journal (Refereed)
    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

  • 29.
    Hussien, Abdelazim
    et al.
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. 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 Algorithm2023In: CMES - Computer Modeling in Engineering & Sciences, ISSN 1526-1492, E-ISSN 1526-1506, Vol. 136, no 3, p. 2267-2289Article in journal (Refereed)
    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.

  • 30.
    Iqbal, Wasif
    et al.
    Natl Univ Sci & Technol NUST, Pakistan.
    Mahmood, Mariam
    Natl Univ Sci & Technol NUST, Pakistan.
    Iqbal, Sheeraz
    Univ Azad Jammu & Kashmir, Pakistan.
    Ali, Majid
    Natl Univ Sci & Technol NUST, Pakistan.
    Iqbal, Muhammad Haroon
    Natl Univ Sci & Technol NUST, Pakistan.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt; Middle East Univ, Jordan.
    Kamel, Salah
    Aswan Univ, Egypt.
    Techno-economic and emission analysis of solar assisted desiccant dehumidification: An experimental and numerical study2023In: Energy Reports, E-ISSN 2352-4847, Vol. 10, p. 2640-2654Article in journal (Refereed)
    Abstract [en]

    In humid climates, it is challenging to maintain moisture content in the air for human thermal comfort and industrial applications. Commercial dehumidifiers rely on conventional electric heaters to regenerate desiccant material, which accounts for significant energy consumption by such dehumidifiers. As a green solution to this problem, the present study integrates a flat plate solar air collector (FPSAC) with a desiccant dehumidifier to effectively use solar thermal energy and reduce electrical consumption. Performance evaluation of glazed and unglazed FPSAC-assisted desiccant dehumidifier has been conducted at process air flow rates of 33, 51 and 62 m3/h with a constant regeneration flow rate of 42 m3/h. Both glazed and unglazed FPSAC assisted desiccant dehumidification systems had the highest dehumidification effectiveness and percentage increase in temperature at the flow rate of 33 m3/h, while the highest moisture removal capacity was at 51 m3/h. Maximum dehumidification effectiveness, percentage temperature increase, and moisture removal capacity for the glazed case were 0.4, 66.67%, and 6.14 kg/h, respectively. Experimental results showed that the glazed FPSAC-integrated desiccant dehumidification system outperforms unglazed FPSAC in all performance evaluation parameters. Using Transient System Simulation software (TRNSYS), the proposed glazed and unglazed assisted desiccant dehumidification system was modeled and validated with experimental results. Furthermore, a techno-economic analysis of the solar hybrid desiccant dehumidification system has been carried out. The FPSAC used in this study showcased a 33.57% yearly solar fraction with a solar hybrid system having a payback period of 4.23 years. In addition, the hybrid system can reduce greenhouse gas emissions yearly by 0.352 tons of CO2 equivalents.

  • 31.
    Izci, Davut
    et al.
    Batman Univ, Turkiye; Middle East Univ, Jordan.
    Ekinci, Serdar
    Batman Univ, Turkiye.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt.
    An elite approach to re-design Aquila optimizer for efficient AFR system control2023In: PLOS ONE, E-ISSN 1932-6203, Vol. 18, no 9, article id e0291788Article in journal (Refereed)
    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.

  • 32.
    Izci, Davut
    et al.
    Batman Univ, Turkiye; Middle East Univ, Jordan.
    Ekinci, Serdar
    Batman Univ, Turkiye.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt.
    Effective PID controller design using a novel hybrid algorithm for high order systems2023In: PLOS ONE, E-ISSN 1932-6203, Vol. 18, no 5, article id e0286060Article in journal (Refereed)
    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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  • 33.
    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
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt.
    Enhancing time-domain performance of vehicle cruise control system by using a multi-strategy improved RUN optimizer2023In: Alexandria Engineering Journal, ISSN 1110-0168, E-ISSN 2090-2670, Vol. 80, p. 609-622Article in journal (Refereed)
    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.

  • 34.
    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
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. 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 approach2023In: Energy Reports, E-ISSN 2352-4847, Vol. 10, p. 1198-1210Article in journal (Refereed)
    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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  • 35.
    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
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
    Awwad, Emad Mahrous
    King Saud Univ, Saudi Arabia.
    Sharaf, Mohamed
    King Saud Univ, Saudi Arabia.
    Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective2023In: BIOMIMETICS, ISSN 2313-7673, Vol. 8, no 3, article id 294Article in journal (Refereed)
    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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  • 36.
    Mostafa, Reham R.
    et al.
    Mansoura Univ, Egypt.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt.
    Khan, Muhammad Attique
    HITEC Univ, Pakistan.
    Kadry, Seifedine
    Noroff Univ Coll, Norway.
    Hashim, Fatma A.
    Helwan Univ, Egypt.
    Enhanced COOT optimization algorithm for Dimensionality Reduction2022In: 2022 FIFTH INTERNATIONAL CONFERENCE OF WOMEN IN DATA SCIENCE AT PRINCE SULTAN UNIVERSITY (WIDS-PSU 2022), IEEE , 2022, p. 43-48Conference paper (Refereed)
    Abstract [en]

    COOT algorithm is a recent metaheuristic algorithm that simulates American coot birds when moving in the sea. However, the COOT algorithm like other metaheuristic techniques may be stuck in local regions. In this study, a modified COOT algorithm called (mCOOT) is presented which is based on 2 techniques: Opposition-based Learning (OBL) & Orthogonal Learning to overcome these limitations. Moreover, to test the novel algorithm called mCOOT, we apply it to the dimensionality reduction problem using 9 UCI datasets and compare it with the original algorithm and 3 other ones. Results prove the effectivness and superiority of the proposed algorithm in solving feature selection in terms of classification accuracy and selected features numbers.

  • 37.
    Saber, Abeer
    et al.
    Damietta Univ, Egypt.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. 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 images2023In: Scientific Reports, E-ISSN 2045-2322, Vol. 13, no 1, article id 14877Article in journal (Refereed)
    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.

  • 38.
    Sasmal, Buddhadev
    et al.
    Midnapore Coll Autonomous, India.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. 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 Optimizer2023In: Archives of Computational Methods in Engineering, ISSN 1134-3060, E-ISSN 1886-1784Article in journal (Refereed)
    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.

  • 39.
    Sasmal, Buddhadev
    et al.
    Midnapore Coll Autonomous, India; Midnapore City Coll, India.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt; Middle East Univ, Jordan.
    Das, Arunita
    Midnapore Coll Autonomous, India.
    Dhal, Krishna Gopal
    Midnapore Coll Autonomous, India.
    Saha, Ramesh
    VIT Bhopal Univ, India.
    Reptile Search Algorithm: Theory, Variants, Applications, and Performance Evaluation2023In: Archives of Computational Methods in Engineering, ISSN 1134-3060, E-ISSN 1886-1784Article in journal (Refereed)
    Abstract [en]

    Reptile Search Algorithm (RSA) is a recently developed nature-inspired meta-heuristics optimization algorithm inspired by the encircling mechanism, hunting mechanism and social behaviours of crocodiles in nature. Since Abualigah et al. introduced RSA in 2022, it has garnered significant interest from researchers and been widely employed to address various optimization challenges across a variety of fields. This is because it has an adequate execution time, an efficient convergence rate, and is more effective than other well-known optimization algorithms. As a result, the objective of this study is to provide an updated survey on the topic. This study provides a comprehensive report of the classical RSA, and its improved variants and their applications in various domains. To adequately analyse RSA, a comprehensive comparison among RSA and its peer NIOAs is performed using mathematical benchmark functions.

  • 40.
    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
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt; Middle East Univ, Jordan.
    Allocation of distributed generator in power networks through an enhanced jellyfish search algorithm2023In: Energy Reports, E-ISSN 2352-4847, Vol. 10, p. 4761-4780Article in journal (Refereed)
    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.

  • 41.
    Shah, Syed Qasim Ali
    et al.
    Riphah Int Univ Islamabad, Pakistan.
    Waris, Umra
    Univ Management & Technol, Pakistan.
    Ahmed, Sheraz
    Univ Management & Technol, Pakistan.
    Agyekum, Ephraim Bonah
    Ural Fed Univ Named First President Russia Boris, Russia.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt; Middle East Univ, Jordan.
    Kamal, Mustafa
    Saudi Elect Univ, Saudi Arabia.
    Rehman, Masood ur
    Saudi Elect Univ, Saudi Arabia.
    Kamel, Salah
    Aswan Univ, Egypt.
    What is the role of remittance and education for environmental pollution?-Analyzing in the presence of financial inclusion and natural resource extraction2023In: Heliyon, E-ISSN 2405-8440, Vol. 9, no 6, article id e17133Article in journal (Refereed)
    Abstract [en]

    This study assessed the impact of gross domestic product (GDP), education, natural resources, remittances, and financial inclusion on carbon emissions in G-11 countries from 1990 to 2021. Based on the negative impact of pollution and the need for sustainable development, this study examined factors affecting CO2 emissions in G-11 countries using non-linear panel ARDL model. The study found that a positive GDP shock increases CO2 emissions in the short and long term, while a negative shock decreases emissions in the short term and increases emissions in the long term. Education was found to increase CO2 emissions in the long term but decrease them in the short term, emphasizing the need for education on combating emissions. Natural resources were also found to increase emissions in the long term, highlighting the need for government-defined institutions to minimize extraction effects and enforce transparency and accountability. Positive changes in personal remittances and financial inclusion were found to increase emissions in both the short and long term, suggesting the need for policies that encourage renewable energy sources and energy efficiency improvement. The study concludes that policymakers should prioritize efficient resource allocation, promote renewable energy usage, and enhance environmental awareness to achieve sustainable development goals in G-11 countries. The possible applications of this study include the use of the models to investigate the asymmetric effects on CO2 emissions. This model can be applied in future studies to examine the relationship between GDP, education, natural resources, personal remittances, financial inclusion, and CO2 emissions in other countries.

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  • 42.
    Singh, Simrandeep
    et al.
    UCRD Chandigarh Univ, India; IIT Ropar, India.
    Singh, Harbinder
    Chandigarh Engn Coll, India; Chandigarh Univ, India.
    Mittal, Nitin
    Shri Vishwakarma Skill Univ, India.
    Singh, Harbinder
    Chandigarh Engn Coll, India; Chandigarh Univ, India.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt.
    Sroubek, Filip
    Czech Acad Sci, Czech Republic.
    A feature level image fusion for Night-Vision context enhancement using Arithmetic optimization algorithm based image segmentation2022In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 209, article id 118272Article in journal (Refereed)
    Abstract [en]

    Images are fused to produce a composite image by combining key characteristics of the source images in image fusion. It makes the fused image better for human vision and machine vision. A novel procedure of Infrared (IR) and Visible (Vis) image fusion is proposed in this manuscript. The main challenges of feature level image fusion are that it will introduce artifacts and noise in the fused image. To preserve the meaningful information without adding artifacts from the source input images, weight map computed from Arithmetic optimization algorithm (AOA) is used for the image fusion process. In this manuscript, feature level fusion is performed after refining the weight maps using a weighted least square optimization (WLS) technique. Through this, the derived salient object details are merged into the visual image without introducing distortion. To affirm the validity of the proposed methodology simulation results are carried for twenty-one image data sets. It is concluded from the qualitative and quantitative experimental analysis that the proposed method works well for most of the image data sets and shows better performance than certain traditional existing models.

  • 43.
    Wang, Shuang
    et al.
    Sanming Univ, Peoples R China.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt.
    Jia, Heming
    Sanming Univ, Peoples R China.
    Abualigah, Laith
    Amman Arab Univ, Jordan.
    Zheng, Rong
    Sanming Univ, Peoples R China.
    Enhanced Remora Optimization Algorithm for Solving Constrained Engineering Optimization Problems2022In: Mathematics, E-ISSN 2227-7390, Vol. 10, no 10, article id 1696Article in journal (Refereed)
    Abstract [en]

    Remora Optimization Algorithm (ROA) is a recent population-based algorithm that mimics the intelligent traveler behavior of Remora. However, the performance of ROA is barely satisfactory; it may be stuck in local optimal regions or has a slow convergence, especially in high dimensional complicated problems. To overcome these limitations, this paper develops an improved version of ROA called Enhanced ROA (EROA) using three different techniques: adaptive dynamic probability, SFO with Levy flight, and restart strategy. The performance of EROA is tested using two different benchmarks and seven real-world engineering problems. The statistical analysis and experimental results show the efficiency of EROA.

  • 44.
    Wang, Shuang
    et al.
    Putian Univ, Peoples R China; Sanming Univ, Peoples R China.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. 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 problems2023In: JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING, ISSN 2288-5048, Vol. 10, no 6, p. 2147-2176Article in journal (Refereed)
    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

  • 45.
    Yu, Huangjing
    et al.
    Sanming Univ, Peoples R China.
    Jia, Heming
    Sanming Univ, Peoples R China.
    Zhou, Jianping
    Sanming Univ, Peoples R China.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt.
    Enhanced Aquila optimizer algorithm for global optimization and constrained engineering problems2022In: Mathematical Biosciences and Engineering, ISSN 1547-1063, E-ISSN 1551-0018, Vol. 19, no 12, p. 14173-14211Article in journal (Refereed)
    Abstract [en]

    The Aquila optimizer (AO) is a recently developed swarm algorithm that simulates the hunting behavior of Aquila birds. In complex optimization problems, an AO may have slow convergence or fall in sub-optimal regions, especially in high complex ones. This paper tries to overcome these problems by using three different strategies: restart strategy, opposition-based learning and chaotic local search. The developed algorithm named as mAO was tested using 29 CEC 2017 functions and five different engineering constrained problems. The results prove the superiority and efficiency of mAO in solving many optimization issues.

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  • 46.
    Zheng, Rong
    et al.
    Sanming Univ, Peoples R China.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt.
    Jia, He-Ming
    Sanming Univ, Peoples R China.
    Abualigah, Laith
    Amman Arab Univ, Jordan; Univ Sains Malaysia, Malaysia.
    Wang, Shuang
    Sanming Univ, Peoples R China.
    Wu, Di
    Sanming Univ, Peoples R China.
    An Improved Wild Horse Optimizer for Solving Optimization Problems2022In: Mathematics, E-ISSN 2227-7390, Vol. 10, no 8, article id 1311Article in journal (Refereed)
    Abstract [en]

    Wild horse optimizer (WHO) is a recently proposed metaheuristic algorithm that simulates the social behavior of wild horses in nature. Although WHO shows competitive performance compared to some algorithms, it suffers from low exploitation capability and stagnation in local optima. This paper presents an improved wild horse optimizer (IWHO), which incorporates three improvements to enhance optimizing capability. The main innovation of this paper is to put forward the random running strategy (RRS) and the competition for waterhole mechanism (CWHM). The random running strategy is employed to balance exploration and exploitation, and the competition for waterhole mechanism is proposed to boost exploitation behavior. Moreover, the dynamic inertia weight strategy (DIWS) is utilized to optimize the global solution. The proposed IWHO is evaluated using twenty-three classical benchmark functions, ten CEC 2021 test functions, and five real-world optimization problems. High-dimensional cases (D = 200, 500, 1000) are also tested. Comparing nine well-known algorithms, the experimental results of test functions demonstrate that the IWHO is very competitive in terms of convergence speed, precision, accuracy, and stability. Further, the practical capability of the proposed method is verified by the results of engineering design problems.

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    fulltext
  • 47.
    Zheng, Rong
    et al.
    Putian Univ, Peoples R China; Sanming Univ, Peoples R China.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. 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 problems2023In: JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING, ISSN 2288-5048, Vol. 10, no 1, p. 329-356Article in journal (Refereed)
    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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