Particle swarm optimization with adaptive mutation for multimodal optimization

Abstract

Particle swarm optimization (PSO) is a population-based stochastic search algorithm, which has shown a good performance over many benchmark and real-world optimization problem. Like other stochastic algorithms, PSO also easily falls into local optima in solving complex multimodal problems. To help trapped particles escape from local minima, this paper presents a new PSO variant, called AMPSO, by employing an adaptive mutation strategy. To verify the performance of AMPSO, a set of well-known complex multimodal benchmarks are used in the experiments. Simulation results demonstrate that the proposed mutation strategy can efficiently improve the performance of PSO.