The particle swarm optimization (PSO) algorithm application – A review
1 Department of Mechanical Engineering, Cross River University of Technology, Calabar- Nigeria.
2 Department of mechanical engineering, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria.
Review Article
Global Journal of Engineering and Technology Advances, 2020, 03(03), 001-006.
Article DOI: 10.30574/gjeta.2020.3.3.0033
Publication history:
Received on 27 May 2020; revised on 14 June 2020; accepted on 17 June 2020
Abstract:
Particle Swarm Optimization (PSO) is one of the concepts of swarm intelligence inspired by studies in neurosciences, cognitive psychology, social ethology and behavioural sciences, introduced in the domain of computing and artificial intelligence as an innovative collective and distributed intelligent paradigm for solving problems, mostly in the domain of optimization, without centralized control or the provision of a global model. The PSO method has roots in genetic algorithms and evolution strategies and shares many similarities with evolutionary computing such as random generation of populations at system initialization or updating generations at optima search. This paper presents an extensive literature review on the concept of PSO, its application to different systems including electric power systems, modifications of the basic PSO to improve its premature convergence, and its combination with other intelligent algorithms to improve search capacity and reduce the time spent to come out of local optimums.
Keywords:
Swarm; Algorithm; Optimization; Particle; Application
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Copyright © 2020 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0