A Bayesian particle swarm optimization algorithm

被引：0

作者：

Heng Xingchen ^{[1
]}

Qin Zheng ^{[1
]}

Wang Xianhui ^{[1
]}

Shao Liping ^{[1
]}

机构：

[1] Xi An Jiao Tong Univ, Res Inst Comp Software, Xian 710049, Peoples R China

来源：

CHINESE JOURNAL OF ELECTRONICS | 2006年 / 15卷 / 4A期

关键词：

Bayesian probability theory; discrete problem; evolutionary computation; genetic algorithm; particle swarm optimization;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A new discrete Particle swarm optimization (PSO) algorithm based on Bayesian probability theory is proposed to deal with discrete problem. In this algorithm, the trajectories of all particles are changed in the probability that a coordinate will take on a zero or one value and the Bayesian probability theory is introduced as a method of the probability update. The final experiments on Knapsack problem show the algorithm outperforms genetic algorithm and previous version of discrete PSO.

引用

页码：937 / 940

页数：4

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