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An Overview of Mutation Strategies in Particle Swarm Optimization

An Overview of Mutation Strategies in Particle Swarm Optimization

Waqas Haider Bangyal, Jamil Ahmad, Hafiz Tayyab Rauf
Copyright: © 2020 |Volume: 11 |Issue: 4 |Pages: 22
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799802877|DOI: 10.4018/IJAMC.2020100102
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MLA

Bangyal, Waqas Haider, et al. "An Overview of Mutation Strategies in Particle Swarm Optimization." IJAMC vol.11, no.4 2020: pp.16-37. http://doi.org/10.4018/IJAMC.2020100102

APA

Bangyal, W. H., Ahmad, J., & Rauf, H. T. (2020). An Overview of Mutation Strategies in Particle Swarm Optimization. International Journal of Applied Metaheuristic Computing (IJAMC), 11(4), 16-37. http://doi.org/10.4018/IJAMC.2020100102

Chicago

Bangyal, Waqas Haider, Jamil Ahmad, and Hafiz Tayyab Rauf. "An Overview of Mutation Strategies in Particle Swarm Optimization," International Journal of Applied Metaheuristic Computing (IJAMC) 11, no.4: 16-37. http://doi.org/10.4018/IJAMC.2020100102

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Abstract

The Particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic search technique encouraged from the intrinsic manner of bee swarm seeking for their food source. With flexibility for numerical experimentation, the PSO algorithm has been mostly used to resolve diverse kind of optimization problems. The PSO algorithm is frequently captured in local optima meanwhile handling the complex real-world problems. Many authors improved the standard PSO algorithm with different mutation strategies but an exhausted comprehensive overview about mutation strategies is still lacking. This article aims to furnish a concise and comprehensive study of problems and challenges that prevent the performance of the PSO algorithm. It has tried to provide guidelines for the researchers who are active in the area of the PSO algorithm and its mutation strategies. The objective of this study is divided into two sections: primarily to display the improvement of the PSO algorithm with mutation strategies that may enhance the performance of the standard PSO algorithm to great extent and secondly, to motivate researchers and developers to use the PSO algorithm to solve the complex real-world problems. This study presents a comprehensive survey of the various PSO algorithms based on mutation strategies. It is anticipated that this survey would be helpful to study the PSO algorithm in detail for researchers.

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