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Author(s)
Mengshan LI, College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, China, 341000
Huaijin ZHANG, College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, China, 341000
Bingsheng CHEN, College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, China, 341000
Lixin GUAN, College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, China, 341000
Yan WU, College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, China, 341000
Abstract
In view of the recently proposed acceleration particle swarm optimization with strong global search capability, a chaos enhanced particles swarm optimization algorithm based chaos theory is proposed. Hybrid chaotic sequence is introduced to adjust the global learning factor, and the algorithm can further increase the global search ability. The performance of the algorithm is verified by testing four typical multiobjective optimization functions, and compared with the classic noninferiority classification multiobjective genetic algorithm, multiobjective particle swarm optimization algorithm and acceleration particle swarm optimization algorithm. The result shows that the Hybrid chaotic acceleration particle swarm optimization algorithm has faster convergence speed and stronger ability to jump out of local optimization, and the performance is superior.
Keywords
Particle Swarm Optimization; Hybrid chaotic; Acceleration algorithm.
Cite this paper
Mengshan LI,
Huaijin ZHANG,
Bingsheng CHEN,
Lixin GUAN,
Yan WU,
Hybrid chaotic enhanced acceleration particle swarm optimization algorithm, SCIREA Journal of Computer. Vol.
3
, No.
1
,
2018
, pp.
16

30
.
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