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A Cooperative Multi-swarm Particle Swarm Optimizer Based Hidden Markov Model
Titre | A Cooperative Multi-swarm Particle Swarm Optimizer Based Hidden Markov Model |
Publication Type | Journal Article |
Year of Publication | 2021 |
Authors | Aoun, O, A. Afia, E, Talbi, E-G |
Journal | Studies in Computational Intelligence |
Volume | 906 |
Pagination | 315-334 |
Abstract | Particle swarm optimization (PSO) is a population-based stochastic metaheuristic algorithm; it has been successful in dealing with a multitude of optimization problems. Many PSO variants have been created to boost its optimization capabilities, in particular, to cope with more complex problems. In this paper, we provide a new approach of multi-population particle swarm optimization with a cooperation strategy. The proposed algorithm splits the PSO population into four sub swarms and attributes a main role to each one. A machine learning technique is designed as an individual level to allow each particle to determine its suitable swarm membership at each iteration. In a collective level, cooperative rules are designed between swarms to ensure more diversity and realize the better solution using a Master/Slave cooperation scheme. Several simulations are performed on a set of benchmark functions to examine the performances of this approach compared to a multitude of state of the art of PSO variants. Experiments reveal a good computational efficiency of the presented method with distinguishable performances. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
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URL | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097954015&doi=10.1007%2f978-3-030-58930-1_21&partnerID=40&md5=64af5832e5e4b2f749a5c24339a3911a |
DOI | 10.1007/978-3-030-58930-1_21 |
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