Adaptive Mutative Scale Chaos Particles Swarm Optimization Based on Logistic Mapping

doi:10.16182/j.issn1004731x.joss.201710002

Abstract

Abstract: To overcome the shortcomings of Particle Swarm Optimization (PSO), an Adaptive Mutative Scale Chaos Particles Swarm Optimization (ACPSO) based on Logistic Mapping was proposed. The chaos method was used to initialize the particles. The adjustment method of the inertia weight depended on the particle's fitness; it could avoid premature convergence for the particles. When the particles fell into the local optimum, mutative scale chaos optimization strategy was adopted to adjust the optimal particles. To test the effectiveness of the algorithm, three representative algorithms were compared with. The results show that the algorithm has high convergence speed and high precision.

Key words: PSO, mutative scale, chaos optimization, adaptive adjustment, learning factor, inertia weight

CLC Number:

TP391.9

Zeng Yanyang, Feng Yunxia, Zhao Wentao. Adaptive Mutative Scale Chaos Particles Swarm Optimization Based on Logistic Mapping[J]. Journal of System Simulation, 2017, 29(10): 2241-2246.

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