An effective hybrid optimization algorithm for the flow shop scheduling problem

被引：0
|
作者：
Sun Kai [1 ]
Yang Genke [1 ]
机构：
[1] Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200030, Peoples R China
来源：
2006 IEEE INTERNATIONAL CONFERENCE ON INFORMATION ACQUISITION, VOLS 1 AND 2, CONFERENCE PROCEEDINGS | 2006年
关键词：
parallel particle swarm optimization; simulated annealing; hybrid optimization algorithm; flow shop scheduling problem;
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：
This paper presents a new multi-swarm co-evolutionary algorithm named parallel particle swam optimization (PPSO) on the basis of standard PSO algorithm. Simulated annealing (SA) algorithm was introduced to increase escaping probability from local optima. By reasonably combining the PPSO with SA, we develop a general, fast and easily implemented hybrid optimization algorithm, and apply it to solve flow shop scheduling problem. Comparing results indicate that the new hybrid method is an effective and competitive approach for the flow shop scheduling problem.
引用
收藏
页码：1234 / 1238
页数：5
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