Bare Bones Particle Swarm Optimization with Considering More Local Best Particles

被引:0
|
作者
Chang, Yen-Ching [1 ,2 ]
Chueh, Chin-Chen [1 ]
Xu, Yongxuan [1 ]
Hsieh, Cheng-Hsueh [1 ]
Chen, Yi-Lin [1 ]
Huang, Yu-Tien [1 ]
Xie, Chengting [1 ]
机构
[1] Chung Shan Med Univ, Dept Med Informat, Taichung, Taiwan
[2] Chung Shan Med Univ Hosp, Dept Med Imaging, Taichung 40201, Taiwan
来源
2013 2ND INTERNATIONAL SYMPOSIUM ON INSTRUMENTATION AND MEASUREMENT, SENSOR NETWORK AND AUTOMATION (IMSNA) | 2013年
关键词
optimization; particle swarm; algorithm; particle swarm optimization; CONVERGENCE ANALYSIS;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Recently, a study of particle swarm optimization (PSO) with considering more local best particles has been proposed to improve the performance of optimization. Better performance of considering some local best particles shows that the proposed two types of variants of PSO have potential advantages over the standard PSO. The basic logic is to exploit all existing resources as fully as possible. Taking the same line, we further study how other local best particles work on bare bones PSO (BBPSO) in this paper. Experimental results show that the adopted idea does effectively raise the overall performance of optimization in most cases.
引用
收藏
页码:1105 / 1108
页数:4
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