Particle swarm optimisation with adaptive selection of inertia weight strategy

Particle swarm optimisation (PSO) is a powerful metaheuristics method that is motivated by the collective behaviour of the intelligence swarms. The lack of velocity controls is a major drawback of the PSO. Inertia weight is a parameter that is commonly used to control the particle speed. In this paper, an adaptive selection of inertia weight strategy is proposed. A set of inertia weight strategy is placed in a candidate pool. Each strategy will be chosen by a probability that is based on its success rate. Empirical studies on the ten unconstrained continuous benchmark problems show that the proposed method can improve the ability to avoid local optima, however it does not increase its convergence speed.