Abstract
The presented paper deals with the comparison of selected random updating strategies of inertia weight in particle swarm optimisation. Six versions of particle swarm optimization were analysed on 28 benchmark functions, prepared for the Special Session on Real-Parameter Single Objective Optimisation at CEC2013. The random components of tested inertia weight were generated from Beta distribution with different values of shape parameters. The best analysed PSO version is the multiswarm PSO, which combines two strategies of updating the inertia weight. The first is driven by the temporally varying shape parameters, while the second is based on random control of shape parameters of Beta distribution.
Highlights
The particle swarm optimisation—PSO—is a popular heuristic optimisation algorithm developed by Kennedy and Eberhart [1]
The PSO oriented research focuses on the development of new adaptation strategies, which avoid the premature convergence of particle population, or being trapped in local optima
The tested single and multiswarm versions of particle swarm optimisation are based on modifications of inertia weight, which are related to the random component controlled by the Beta distribution
Summary
The particle swarm optimisation—PSO—is a popular heuristic optimisation algorithm developed by Kennedy and Eberhart [1]. The random adaptations of inertia weight play an important role in improving the PSO performance [4, 11, 12, 14, 15]. They support the exploratory search in the beginning of optimisation process. The tested single and multiswarm versions of particle swarm optimisation are based on modifications of inertia weight, which are related to the random component controlled by the Beta distribution. The description of PSO provides details on standard PSO, the proposed random inertia weight strategies, and the description of tested multiswarm PSO.
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