Title: On the effect of particle update modes in particle swarm optimisation

Authors: Nanjiang Dong; Rui Wang; Tao Zhang; Junwei Ou

Addresses: The College of Systems Engineering, National University of Defense Technology, Changsha, China ' The College of Systems Engineering, National University of Defense Technology, Changsha, China ' The College of Systems Engineering, National University of Defense Technology, Changsha, China ' The College of Systems Engineering, National University of Defense Technology, Changsha, China

Abstract: Particle swarm optimisation has been successfully applied in various single- and multi-objective optimisation problems. Through the literature review, it is shown that in PSO-based algorithms particles are updated mainly in two different modes. Specifically, the first mode denoted as PSO-a uses random vectors in [0, 1]ⁿ in the particle update process. The second mode denoted as PSO-b uses random variables in [0, 1]. This study systematically analysed the effect of different modes on a varied set of benchmarks. Experimental results show that the PSO-a mode is more suitable for single-objective optimisation while the PSO-b has certain advantages for multi-objective optimisation due to the regularity of multi-objective problems. Also, the introduction of a mutation operator into PSO-b can overcome the limit of dimension. Moreover, to guarantee finding the optimal solution, the swarm size must be larger than the problem dimensionality when PSO-b is purely adopted.

Keywords: evolutionary computation? particle swarm optimisation? PSO? population size? multi-objective optimisation.

DOI: 10.1504/IJBIC.2023.132784

International Journal of Bio-Inspired Computation, 2023 Vol.21 No.4, pp.230 - 239

Received: 30 Jun 2022
Accepted: 26 Dec 2022
Published online: 09 Aug 2023 *

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