Particle swarm optimization with adaptive mutation for multimodal optimization
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摘要
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.
论文关键词:Particle swarm optimization (PSO),Adaptive mutation,Multimodal optimization,Global optimization
论文评审过程:Available online 20 July 2013.
论文官网地址:https://doi.org/10.1016/j.amc.2013.06.074