期刊
APPLIED MATHEMATICS AND COMPUTATION
卷 221, 期 -, 页码 296-305出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.amc.2013.06.074
关键词
Particle swarm optimization (PSO); Adaptive mutation; Multimodal optimization; Global optimization
资金
- Humanity and Social Science Foundation of Ministry of Education of China [13YJCZH174]
- Science and Technology Plan Project of Jiangxi Provincial Education Department [GJJ13744]
- National Natural Science Foundation of China [61070008, 61175127, 61261039]
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. (C) 2013 Elsevier Inc. All rights reserved.
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