Adaptive Mutative Scale Chaos Particles Swarm Optimization Based on Logistic Mapping

Authors

Yanyang Zeng, College of Computer Science and Technology of Henan Polytechnic University, Jiaozuo 454000, China;
Yunxia Feng, College of Computer Science and Technology of Henan Polytechnic University, Jiaozuo 454000, China;
Wentao Zhao, College of Computer Science and Technology of Henan Polytechnic University, Jiaozuo 454000, China;

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.

Recommended Citation

Zeng, Yanyang; Feng, Yunxia; and Zhao, Wentao (2020) "Adaptive Mutative Scale Chaos Particles Swarm Optimization Based on Logistic Mapping," Journal of System Simulation: Vol. 29: Iss. 10, Article 2.
DOI: 10.16182/j.issn1004731x.joss.201710002
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss10/2

First Page

2241

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201710002

Last Page

2246

CLC

TP391.9

Recommended Citation

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.

DOI

10.16182/j.issn1004731x.joss.201710002