On the effect of particle update modes in particle swarm optimisation Online publication date: Wed, 09-Aug-2023
by Nanjiang Dong; Rui Wang; Tao Zhang; Junwei Ou
International Journal of Bio-Inspired Computation (IJBIC), Vol. 21, No. 4, 2023
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Bio-Inspired Computation (IJBIC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook