Open AccessNew Particle Swarm Optimisation Algorithm with Hénon Chaotic Map Structure
Author(s) -
Yan Tao,
Liu Fengxian,
Chen Bin
Publication year - 2017
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2017.06.006
Subject(s) - particle swarm optimization , algorithm , chaotic , benchmark (surveying) , convergence (economics) , hybrid algorithm (constraint satisfaction) , computer science , swarm behaviour , premature convergence , combing , mathematical optimization , rate of convergence , mathematics , artificial intelligence , key (lock) , constraint logic programming , cartography , geodesy , computer security , economic growth , stochastic programming , economics , constraint programming , geography
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.