Multi-objective particle swarm optimization algorithm using Cauchy mutation and improved crowding distance
International Journal of Intelligent Computing and Cybernetics
ISSN: 1756-378X
Article publication date: 22 August 2022
Issue publication date: 15 May 2023
Abstract
Purpose
Multi-objective is a complex problem that appears in real life while these objectives are conflicting. The swarm intelligence algorithm is often used to solve such multi-objective problems. Due to its strong search ability and convergence ability, particle swarm optimization algorithm is proposed, and the multi-objective particle swarm optimization algorithm is used to solve multi-objective optimization problems. However, the particles of particle swarm optimization algorithm are easy to fall into local optimization because of their fast convergence. Uneven distribution and poor diversity are the two key drawbacks of the Pareto front of multi-objective particle swarm optimization algorithm. Therefore, this paper aims to propose an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.
Design/methodology/approach
In this paper, the proposed algorithm uses adaptive Cauchy mutation and improved crowding distance to perturb the particles in the population in a dynamic way in order to help the particles trapped in the local optimization jump out of it which improves the convergence performance consequently.
Findings
In order to solve the problems of uneven distribution and poor diversity in the Pareto front of multi-objective particle swarm optimization algorithm, this paper uses adaptive Cauchy mutation and improved crowding distance to help the particles trapped in the local optimization jump out of the local optimization. Experimental results show that the proposed algorithm has obvious advantages in convergence performance for nine benchmark functions compared with other multi-objective optimization algorithms.
Originality/value
In order to help the particles trapped in the local optimization jump out of the local optimization which improves the convergence performance consequently, this paper proposes an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.
Keywords
Acknowledgements
This work was supported by the Key Project of Science and Technology Innovation (2030) supported by the Ministry of Science and Technology of China (Grant No. 2018AAA0101301), the Key Projects of Artificial Intelligence of High School in Guangdong Province (No. 2019KZDZX1011), Innovation Project of High School in Guangdong Province (No. 2018KTSCX314), Dongguan Social Development Science and Technology Project (No. 20211800904722) and Dongguan Science and Technology Special Commissioner Project (No. 20201800500442).
Citation
Li, Q., Zeng, X. and Wei, W. (2023), "Multi-objective particle swarm optimization algorithm using Cauchy mutation and improved crowding distance", International Journal of Intelligent Computing and Cybernetics, Vol. 16 No. 2, pp. 250-276. https://doi.org/10.1108/IJICC-04-2022-0118
Publisher
:Emerald Publishing Limited
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