An Improved Multi-Population Hybrid Particle Swarm Optimization for Flexible Job-Shop Scheduling Problem

被引：0
|
作者：
Chen, Wen-xian [1 ]
Luo, De-lin [1 ]
Guo, Jian-min [1 ]
Chen, Jin [1 ]
机构：
[1] Xiamen Univ, Dept Automat, Xiamen 361005, Peoples R China
来源：
PROCEEDING OF THE 10TH INTERNATIONAL CONFERENCE ON INTELLIGENT TECHNOLOGIES | 2009年
关键词：
Fuzzy Objective; Flexible Job-Shop Scheduling; Hybrid PSO; Multi-Population;
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：
Considering computational complexity, restrictive conditions and probabilities of falling into local optimum for general algorithms in flexible job-shop scheduling problem, a method based on multi-population hybrid particle swarm optimization is proposed. For the new method, searching efficiency is improved and the best processing sequence is found for flexible job-shop scheduling via simultaneous evolution of multiple populations. By computational simulation, the method is proved to be valid. A new perspective for flexible job-shop scheduling problem is given.
引用
收藏
页码：620 / 624
页数：5
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