On the effect of particle update modes in particle swarm optimisation

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.