Distributed learning particle swarm optimizer for global optimization of multimodal problems

作者:Geng Zhang, Yangmin Li, Yuhui Shi

摘要

Particle swarm optimizer (PSO) is an effective tool for solving many optimization problems. However, it may easily get trapped into local optimumwhen solving complex multimodal nonseparable problems. This paper presents a novel algorithm called distributed learning particle swarm optimizer (DLPSO) to solve multimodal nonseparable problems. The strategy for DLPSO is to extract good vector information from local vectors which are distributed around the search space and then to form a new vector which can jump out of local optima and will be optimized further. Experimental studies on a set of test functions show that DLPSO exhibits better performance in solving optimization problems with few interactions between variables than several other peer algorithms.

论文关键词:particle swarm optimizer (PSO), orthogonal experimental design (OED), swarm intelligence

论文评审过程:

论文官网地址:https://doi.org/10.1007/s11704-016-5373-1

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