The Particle Swarm Optimization Algorithm with Adaptive Chaos Perturbation

  • Li Mengxia
  • Liao Ruiquan 1. Petroleum Engineering College, Yangtze University Wuhan Hubei 430100, China 2. The Branch of Key Laboratory of CNPC for Oil and Gas Production, Yangtze University Wuhan Hubei 430100, China 3. Key Laboratory of Exploration Technologies for Oil and Gas Resources, Yangtze University Wuhan Hubei 430100, China
  • Dong Yong 1. School of Information and Mathematics,Yangtze University Jingzhou Hubei 434023, China 2. The Branch of Key Laboratory of CNPC for Oil and Gas Production, Yangtze University Wuhan Hubei 430100, China 3. Key Laboratory of Exploration Technologies for Oil and Gas Resources, Yangtze University Wuhan Hubei 430100, China *Corresponding author:

Abstract

Aiming at the two characteristics of premature convergence of particle swarm optimization that the particle velocity approaches 0 and particle swarm congregate, this paper learns from the annealing function of the simulated annealing algorithm and adaptively and dynamically adjusts inertia weights according to the velocity information of particles to avoid approaching 0 untimely. This paper uses the good uniformity of Anderson chaotic mapping and performs chaos perturbation to part of particles based on the information of variance of the population’s fitness to avoid the untimely aggregation of particle swarm. The numerical simulations of five test functions are performed and the results are compared with several swarm intelligence heuristic algorithms. The results shows that the modified algorithm can keep the population diversity well in the middle stage of the iterative process and it can improve the mean best of the algorithm and the success rate of search.

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Published
2016-10-17
How to Cite
MENGXIA, Li; RUIQUAN, Liao; YONG, Dong. The Particle Swarm Optimization Algorithm with Adaptive Chaos Perturbation. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 11, n. 6, p. 804-818, oct. 2016. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/2525>. Date accessed: 13 july 2020. doi: https://doi.org/10.15837/ijccc.2016.6.2525.

Keywords

Particle Swarm Optimization, inertia weight,population diversity, expected velocity, chaos perturbation