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 initial population. On the other… More >
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