New Modified Bare-bones Particle Swarm Optimization

被引:0
|
作者
Zhao, Xinchao [1 ]
Liu, Huiping [1 ]
Liu, Dongyue [1 ]
Ai, Wenbao [1 ]
Zuo, Xingquan [2 ]
机构
[1] Beijing Univ Posts & Telecommun, Sch Sci, Beijing 100876, Peoples R China
[2] Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing 100876, Peoples R China
关键词
Particle swarm optimization(PSO); Bare-bones PSO; Gaussian random perturbation; Swarm intelligence; GLOBAL OPTIMIZATION;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Bare-bones Particle Swarm Optimization (BPSO) is a simplified PSO variant, which has shown potential performance on many multimodal optimization problems. However, BPSO is also possible to be trapped into local optima for high-dimensional and complicated optimization problems. In order to enhance the performance of BPSO, this paper presents a modified BPSO, called NMBPSO. It combined the ideas of the traditional PSO and a modified BPSO to improve the capacity of balancing exploration and exploitation during the search process. To verify the effect and benefit of the proposed algorithm, a set of well known benchmark functions are employed and compared against some competitive PSO variants. Experiment results indicate that NMBPSO performs better than the traditional PSO, BPSO and a modified BPSO algorithm.
引用
收藏
页码:416 / 422
页数:7
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