Book Chapters

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Browsing Book Chapters by Subject "applications"

Now showing 1 - 4 of 4
  • Publication
    Animal migration optimization algorithm: novel optimizer, analysis, and applications
    (2024)
    Abualigah, Laith
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    Ahmad, Esraa Nasser
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    Ikotun, Abiodun M.
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    Abu Zitar, Raed 
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    Alsoud, Anas Ratib
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    Khodadadi, Nima
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    Ezugwu, Absalom E.
    ;
    Jia, Heming
    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.
    Scopus© Citations 1  16
  • Publication
    Arithmetic optimization algorithm: a review and analysis
    (2024)
    Abualigah, Laith
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    Abusaleem, Aya
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    Ikotun, Abiodun M.
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    Abu Zitar, Raed 
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    Alsoud, Anas Ratib
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    Khodadadi, Nima
    ;
    Ezugwu, Absalom E.
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    Hanandeh, Essam Said
    ;
    Jia, Heming
    A metaheuristic algorithm called arithmetic optimization algorithm (AOA) is developed to solve various optimization problems. It is inspired by various math operators, such as subtraction, division, addition, and multiplication. As a result, it can perform diverse optimization tasks in different search spaces. This article reviews the behaviors of mathematics operations that inspire the main features of AOA, which is a metaheuristic algorithm. This paper reviews the original version of the algorithm and discusses the various variants of the algorithm. It also explores the applications of the algorithm in different fields, which will be of help for future researchers.
      21
  • Publication
    Particle swarm optimization algorithm: review and applications
    (2024)
    Abualigah, Laith
    ;
    Sheikhan, Ahlam
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    M. Ikotun, Abiodun
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    Abu Zitar, Raed 
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    Alsoud, Anas Ratib
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    Al-Shourbaji, Ibrahim
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    Hussien, Abdelazim G.
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    Jia, Heming
    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.
    Scopus© Citations 4  12
  • Publication
    Social spider optimization algorithm: survey and new applications
    (2024)
    Abualigah, Laith
    ;
    Al Turk, Ahmad A.
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    Ikotun, Abiodun M.
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    Abu Zitar, Raed 
    ;
    Alsoud, Anas Ratib
    ;
    Khodadadi, Nima
    ;
    Hussien, Abdelazim G.
    ;
    Jia, Heming
    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.
      13