The development of new metaheuristic algorithms and their enhancements has seen significant growth, yet many of these algorithms share similar limitations. This is largely due to insufficient studies analyzing their structures and performance prior to proposing modifications. The Tunicate Swarm Algorithm (TSA), a recently developed nature-inspired algorithm, offers a simple structure, distinctive stabilizing features, and impressive efficiency. Inspired by the social behaviors of tunicates and their jet propulsion for movement and foraging, the TSA employs a dynamic weighting mechanism to simulate their influence during the search process. Its notable traits, including simplicity, adaptability, minimal parameters, and independence from derivatives, have contributed to its rapid adoption across various optimization problems. This review focuses on the foundational research underlying the TSA, exploring its development and effectiveness as highlighted in existing studies. It also examines enhancements to the algorithm's behavior, particularly efforts to align search space geometry with practical optimization challenges. Finally, potential directions for future improvements and adaptations are proposed to further advance the TSA's capabilities.

Metaheuristics are powerful optimization techniques that have gained significant attention for their ability to solve complex and large-scale problems where exact algorithms fall short. These methods, including evolutionary algorithms, swarm intelligence, physics-based, and Human-based, are inspired by natural processes and are particularly effective for problems with vast search spaces and multiple constraints. In engineering, metaheuristics are frequently applied to optimize resource allocation, scheduling, and design processes, where traditional methods are computationally intensive or impractical. In cloud computing, task scheduling remains a critical challenge as demand for scalable, high-performance, and cost-effective solutions grows. Metaheuristic optimization offers promising approaches to address the scale, heterogeneity, and dynamic nature of cloud environments.

The increasing reliance on cloud-based systems across industries has amplified the need for efficient task scheduling and resource management solutions. Traditional scheduling approaches often lack the flexibility and adaptability required to handle the dynamic workloads of cloud environments, leading to inefficiencies in resource utilization and task execution time. Motivated by these challenges, this research explores how metaheuristic optimization can enhance cloud task scheduling by improving performance, balancing loads, and minimizing costs. This thesis aims to develop innovative optimization techniques that address these pressing issues, contributing to more robust and adaptive scheduling frameworks for cloud systems.

This thesis is organized into two main parts. The first part provides a theoretical foundation, offering background on optimization methods, an overview of engineering problems, and a discussion of task scheduling challenges in cloud computing. The second part comprises three published studies that illustrate the practical application of the proposed methods. Paper I and II present the Enhanced Evaporation rate Water Cycle Algorithm (EErWCA) and modified Artificial Electric Field Algorithm (mAEFA) techniques for addressing global optimization and engineering problems. Paper III develops hybrid Artificial Gorilla Troops Optimizer with Honey Badger Algorithm (GTOHBA) for optimized cloud task scheduling. Together, these contributions address key research questions, positioning this work within the broader context of optimization-driven scheduling and cloud computing.

Eye disease detection has achieved significant advancements thanks to artificial intelligence (AI) techniques. However, the construction of high-accuracy predictive models still faces challenges, and one reason is the deficiency of the optimizer. This paper presents an efficient optimizer named Success History Adaptive Competitive Swarm Optimizer with Linear Population Reduction (L-SHACSO). Inspired by the effective success history adaptation scheme and linear population reduction strategy in Differential Evolution (DE), we introduce these techniques into CSO to enable the automatic and intelligent adjustment of hyper-parameters during optimization thereby balancing exploration and exploitation across different phases. To thoroughly investigate the performance of L-SHACSO, we conduct extensive numerical experiments on CEC2017, CEC2020, CEC2022, and eight engineering problems. State-of-the-art optimizers including jSO and L-SHADE-cnEpSin and recently proposed metaheuristic algorithms (MAs) such as RIME and the Parrot Optimizer (PO) are employed as competitors. Experimental results confirm the superiority of L-SHACSO across various optimization tasks. Furthermore, we integrate L-SHACSO into DenseNet and Extreme Learning Machine (ELM) and propose DenseNet-L-SHACSO-ELM for eye disease detection, where the features extracted by the pre-trained DenseNet are fed into L-SHACSO-optimized ELM for classification. Experiments on public datasets confirm the feasibility and effectiveness of our proposed model, which has great potential in real-world scenarios. The source code of this research is available at https://github.com/RuiZhong961230/L-SHACSO.

Cloud computing has revolutionized the way a variety of ubiquitous computing resources are provided to users with ease and on a pay-per-usage basis. Task scheduling problem is an important challenge, which involves assigning resources to users' Bag-of-Tasks applications in a way that maximizes either system provider or user performance or both. With the increase in system size and the number of applications, the Bag-of-Tasks scheduling (BoTS) problem becomes more complex due to the expansion of search space. Such a problem falls in the category of NP-hard optimization challenges, which are often effectively tackled by metaheuristics. However, standalone metaheuristics generally suffer from certain deficiencies which affect their searching efficiency resulting in deteriorated final performance. This paper aims to introduce an optimal hybrid metaheuristic algorithm by leveraging the strengths of both the Artificial Gorilla Troops Optimizer (GTO) and the Honey Badger Algorithm (HBA) to find an approximate scheduling solution for the BoTS problem. While the original GTO has demonstrated effectiveness since its inception, it possesses limitations, particularly in addressing composite and high-dimensional problems. To address these limitations, this paper proposes a novel approach by introducing a new updating equation inspired by the HBA, specifically designed to enhance the exploitation phase of the algorithm. Through this integration, the goal is to overcome the drawbacks of the GTO and improve its performance in solving complex optimization problems. The initial performance of the GTOHBA algorithm tested on standard CEC2017 and CEC2022 benchmarks shows significant performance improvement over the baseline metaheuristics. Later on, we applied the proposed GTOHBA on the BoTS problem using standard parallel workloads (CEA-Curie and HPC2N) to optimize makespan and energy objectives. The obtained outcomes of the proposed GTOHBA are compared to the scheduling techniques based on well-known metaheuristics under the same experimental conditions using standard statistical measures and box plots. In the case of CEA-Curie workloads, the GTOHBA produced makespan and energy consumption reduction in the range of 8.12-22.76% and 6.2-18.00%, respectively over the compared metaheuristics. Whereas for the HPC2N workloads, GTOHBA achieved 8.46-30.97% makespan reduction and 8.51-33.41% energy consumption reduction against the tested metaheuristics. In conclusion, the proposed hybrid metaheuristic algorithm provides a promising solution to the BoTS problem, that can enhance the performance and efficiency of cloud computing systems.

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

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

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

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

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

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