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Particle swarm optimization with adaptive mutation for multimodal optimization
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DOI：10.1016/j.amc.2013.06.074.png)
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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.
Keyword:
Particle swarm optimization (PSO)
Adaptive mutation
Multimodal optimization
Global optimization
期刊
IF:
3.4
论文数:
2.3W
被引数:
3.3W
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OAAI分享收藏Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
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