An improved theta-PSO algorithm with crossover and mutation

Particle swarm optimization (PSO) is an efficient optimization algorithm. A theta-PSO based on phase angle was put forward in our previous work, which has good optimization performance when dealing with some benchmark functions. But this algorithm may easily stick in the local minima sometime when h...

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Bibliographic Details
Published in2008 7th World Congress on Intelligent Control and Automation pp. 5308 - 5312
Main Authors Weimin Zhong, Jianliang Xing, Feng Qian
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2008
Subjects
Acceleration
Automation
benchmark function
Benchmark testing
Crossover
Equations
Mathematical model
Mutation
Optimization
Particle swarm optimization
Phase angle
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Summary:Particle swarm optimization (PSO) is an efficient optimization algorithm. A theta-PSO based on phase angle was put forward in our previous work, which has good optimization performance when dealing with some benchmark functions. But this algorithm may easily stick in the local minima sometime when handling some complex multi-mode functions. To enhance the optimization performance, crossover and mutation operators were introduced in this paper. Benchmark testing of some multi-mode functions shows that this improved theta-PSO can overcome the local minima and achieve the goal of global minimum in limited iterations.
ISBN:1424421136
9781424421138
DOI:10.1109/WCICA.2008.4593793