Dongping Tiana,b
Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 331-342, 2018, DOI:10.1080/10798587.2017.1293881
Abstract Particle swarm optimization (PSO) is a population based swarm intelligence algorithm that has been
deeply studied and widely applied to a variety of problems. However, it is easily trapped into the
local optima and premature convergence appears when solving complex multimodal problems. To
address these issues, we present a new particle swarm optimization by introducing chaotic maps (Tent
and Logistic) and Gaussian mutation mechanism as well as a local re-initialization strategy into the
standard PSO algorithm. On one hand, the chaotic map is utilized to generate uniformly distributed
particles to improve the quality of the… More >
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