YAN Tao, LIU Fengxian, CHEN Bin. New Particle Swarm Optimisation Algorithm with Hénon Chaotic Map Structure[J]. Chinese Journal of Electronics, 2017, 26(4): 747-753. doi: 10.1049/cje.2017.06.006
Citation: YAN Tao, LIU Fengxian, CHEN Bin. New Particle Swarm Optimisation Algorithm with Hénon Chaotic Map Structure[J]. Chinese Journal of Electronics, 2017, 26(4): 747-753. doi: 10.1049/cje.2017.06.006

New Particle Swarm Optimisation Algorithm with Hénon Chaotic Map Structure

doi: 10.1049/cje.2017.06.006
Funds:  This work is supported by the West Light Foundation of The Chinese Academy of Sciences (No.2011180).
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  • Corresponding author: CHEN Bin (corresponding author) was born in Sichuan. He received the Ph.D. degree in computer software and theory from Chengdu Institute of Computer Applications, Chinese Academy of Science. He is now a Ph.D. supervisor. His research interests include image processing and pattern recognition. (Email: bchen@casit.com.cn)
  • Received Date: 2016-05-23
  • Rev Recd Date: 2017-03-31
  • Publish Date: 2017-07-10
  • A new Particle swarm optimisation (PSO) algorithm based on the Hénon chaotic map (hereafter HCPSO algorithm) is presented in this paper to deal with the premature convergence problem of the traditional PSO algorithm. The HCPSO algorithm changes the structure of the traditional PSO algorithm and deviates from the structures of conventional hybrid algorithms that merely introduce chaotic searching into PSO. Based on the convergence condition of PSO, the HCPSO algorithm can improve solution precision and increase the convergence rate by combing using the targeting technique of chaotic mapping. For validation, fourteen benchmark functions were used to compare the proposed algorithm with six other hybrid PSO algorithms. The experimental results indicated that the HCPSO algorithm is superior to the other algorithms in terms of convergence speed and solution accuracy.
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