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Communication Dans Un Congrès Année : 2022

Improved Multi-Particle Swarm Optimization based on multi-exemplar and forgetting ability

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Jaouher Chrouta
  • Fonction : Auteur
Abderrahmen Zaafour
  • Fonction : Auteur

Résumé

Several variants of particle swarm optimization (PSO) have been created to identify various solutions to complicated optimization problems. Only a few PSO algorithms exist that can locate and monitor multiple optima in dynamically shifting search landscapes when dealing with dynamic optimization situations. These methods have yet to be thoroughly tested on a large number of dynamic optimization problems. In fact, because there are so many PSO algorithm modifications, it's simple to get stuck in a local optima. To address the aforementioned flaws, this work proposes and evaluates an enhanced version of the multiswarm particle swarm optimization technique (MsPSO) with numerous variations particle swarm optimization published in the literature. Standard tests and indicators provided in the specialized literature are used to verify the effectiveness of the suggested algorithm. Furthermore, on the CEC'13 test suite, comparison results between the extended heterogeneous multi swarm PSO algorithm (XMsPSO) and other nine popular PSO show that XMsPSO achieves a very optimistic performance for solving various kinds of problems, contributing to both higher solution accuracy.
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Dates et versions

hal-03778928 , version 1 (16-09-2022)

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Jaouher Chrouta, Aymen Aloui, Nadia Hamani, Abderrahmen Zaafour. Improved Multi-Particle Swarm Optimization based on multi-exemplar and forgetting ability. 2022 8TH INTERNATIONAL CONFERENCE ON CONTROL, DECISION AND INFORMATION TECHNOLOGIES (CODIT'22), May 2022, Istanbul, Turkey. pp.1614-1619, ⟨10.1109/CODIT55151.2022.9803929⟩. ⟨hal-03778928⟩

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