Comparing inertia weights in Multi- swarm Particle Swarm Optimization

Intelligent collective behavior of some animals such as flocks of birds and schools of fish inspired stochastic-based collective algorithm such as Multi-swarm Particle Swarm optimisation (MsPSO). In 2014, Ngaam. Cheung developed a swarm intelligence technique based on the adjustment of fewer parameters in which the main parameter is the inertia weight. This technique considerably affects the convergence and exploration exploitation trade-off in MsPSO process. Since that, different strategies for determining the value of inertia weight during a course of run have been proposed. This paper studies 9 relatively recent and popular Inertia Weight strategies and compares their performance on 15 optimization test problems. This paper presents the first comprehensive review of the various inertia weight strategies reported in the related literature.