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An Improved Soft Island Model of the Fish School Search Algorithm with Exponential Step Decay Using Cluster-Based Population Initialization

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  • Liliya A. Demidova

    (Institute for Information Technologies, Federal State Budget Educational Institution of Higher Education, MIREA—Russian Technological University, 78, Vernadsky Avenue, 119454 Moscow, Russia)

  • Vladimir E. Zhuravlev

    (Institute for Information Technologies, Federal State Budget Educational Institution of Higher Education, MIREA—Russian Technological University, 78, Vernadsky Avenue, 119454 Moscow, Russia)

Abstract

Optimization is a highly relevant area of research due to its widespread applications. The development of new optimization algorithms or the improvement of existing ones enhances the efficiency of various fields of activity. In this paper, an improved Soft Island Model (SIM) is considered for the Tent-map-based Fish School Search algorithm with Exponential step decay (ETFSS). The proposed model is based on a probabilistic approach to realize the migration process relying on the statistics of the overall achievement of each island. In order to generate the initial population of the algorithm, a new initialization method is proposed in which all islands are formed in separate regions of the search space, thus forming clusters. For the presented SIM-ETFSS algorithm, numerical experiments with the optimization of classical test functions, as well as checks for the presence of some known defects that lead to undesirable effects in problem solving, have been carried out. Tools, such as the Mann–Whitney U test, box plots and other statistical methods of data analysis, are used to evaluate the quality of the presented algorithm, using which the superiority of SIM-ETFSS over its original version is demonstrated. The results obtained are analyzed and discussed.

Suggested Citation

  • Liliya A. Demidova & Vladimir E. Zhuravlev, 2025. "An Improved Soft Island Model of the Fish School Search Algorithm with Exponential Step Decay Using Cluster-Based Population Initialization," Stats, MDPI, vol. 8(1), pages 1-37, January.
    • Handle: RePEc:gam:jstats:v:8:y:2025:i:1:p:10-:d:1573873
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    References listed on IDEAS

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    1. Chengxian Xu & Jianzhong Zhang, 2001. "A Survey of Quasi-Newton Equations and Quasi-Newton Methods for Optimization," Annals of Operations Research, Springer, vol. 103(1), pages 213-234, March.
    2. Omer Ali & Qamar Abbas & Khalid Mahmood & Ernesto Bautista Thompson & Jon Arambarri & Imran Ashraf, 2023. "Competitive Coevolution-Based Improved Phasor Particle Swarm Optimization Algorithm for Solving Continuous Problems," Mathematics, MDPI, vol. 11(21), pages 1-28, October.
    3. Vladimir Stanovov & Eugene Semenkin, 2024. "Adaptation of the Scaling Factor Based on the Success Rate in Differential Evolution," Mathematics, MDPI, vol. 12(4), pages 1-22, February.
    4. Vladimir Stanovov & Eugene Semenkin, 2023. "Surrogate-Assisted Automatic Parameter Adaptation Design for Differential Evolution," Mathematics, MDPI, vol. 11(13), pages 1-19, June.
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