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- PublicationAnimal migration optimization algorithm: novel optimizer, analysis, and applications(2024)A new heuristic optimization algorithm was proposed in 2013 called the animal migration optimization (AMO) algorithm. It is based on the animal actions and habits in migration seasons. Optimization algorithms are applied to find the optimal solutions in many domains and fields such as image processing, machine learning, and others. In this paper, we present an overview of the AMO algorithm, describe the algorithm procedure, summarize some of the recent works and applications that use AMO algorithms, classify these works in many application domains, and display the robustness points, weak points, and limitations for AMO. This paper can help and direct the researchers in future works in using AMO in their studies.
3 - PublicationSocial spider optimization algorithm: survey and new applications(2024)The behavior of insects and animals has inspired particle swarm optimization (PSO). An insect’s settlement acts as an integrated part that works as a speeded unit, also doing construction for huge projects. Besides the connections between insect societies, they are communicated internally between their members. Each spider has a weight based on the value of fitness. This algorithm consists of two search spiders called agents: males and females. This algorithm has been developed over time, resulting in many versions besides theories and findings. One of the PSO algorithms or versions is the social spider optimization (SSO) algorithm, a simulation of the interaction between spider groups, males and females. Based on gender, evolutionary factors simulate different behaviors usually found at their settlement based on the biological aspect. This survey studied the SSO and compared it with other PSO algorithms to find the best-performing algorithm based on a benchmark. This survey also studied the main applications of this algorithm in different fields, including medical, mathematical, artificial intelligence, engineering, and data engineering, and how this algorithm affected, impacted, and supported the different fields. Finally, this chapter provides an expectation of the fields that need to work with this algorithm to improve problem-solving and the fields that have a growing number studies that use this algorithm.
7 - PublicationParticle swarm optimization algorithm: review and applications(2024)Particle swarm optimization (PSO) is a heuristic global optimization technique and an optimization algorithm that is swarm intelligence-based. It is based on studies into the movement of bird flocks. Individual birds share information about their position, speed, and fitness while searching the food source, and the flock's behavior is affected to enhance the likelihood of migration to high-fitness areas. This paper surveys the published papers in PSO algorithms. Twenty research papers are analyzed and classified according to the implementation area used by the PSO algorithm (neural networks, feature selection, and data clustering). The main procedure of the PSO algorithm is presented. Future researchers can use the collected data in this survey as baseline information on the PSO and PSO's applications.
4 - PublicationSalp swarm algorithm: survey, analysis, and new applications(2024)This chapter offers the sea salmon-associated polyp (SALP) swarm algorithm (SSA) and multipurpose SSA (MSSA) as new optimization algorithms for solving optimization problems with single and multiple objectives. The behavior of the species when traveling and foraging in the waters is the main source of SSA and MSSA. These two algorithms are put to test on a variety of mathematical optimization functions to see how they behave when it comes to finding the best solutions to optimization problems. The results of the mathematical functions reveal that the SSA technique may improve the initial random solutions more effectively and efficiently. The findings of the MSSA method show that it can approach optimal Pareto solutions with strong convergence and coverage. The research also explains how to use SSA and MSSA to solve a number of computationally challenging and expensive engineering design issues (e.g., airfoil design and marine propeller design). The benefits of the proposed algorithms in addressing real-world issues with challenging and unknown search areas are demonstrated by the outcomes of real-world case studies. In this paper, the most important literature and previous studies related to the subject of the study were presented, where nearly 30 researches were referred to develop a theoretical framework related to SSA and other improved algorithms and to compare SSA with other systems. The MSSA approach has been linked to a large number of previously published algorithms. Many standard criteria that require individual and multiple objectives are included, and the most important findings of this study and the most important conclusions related to the subject of the study are included.
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