@article {Aoun2021315,
	title = {A Cooperative Multi-swarm Particle Swarm Optimizer Based Hidden Markov Model},
	journal = {Studies in Computational Intelligence},
	volume = {906},
	year = {2021},
	note = {cited By 1},
	pages = {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. {\textcopyright} 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-030-58930-1_21},
	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},
	author = {Aoun, O. and El Afia, A. and Talbi, E.-G.}
}
