Parallel genetic algorithm for a flow-shop problem with multiprocessor tasks

被引：0
|
作者：
Oguz, C [1 ]
Fung, YF
Ercan, MF
Qi, XT
机构：
[1] Hong Kong Polytech Univ, Dept Management, Hong Kong, Hong Kong, Peoples R China
[2] Hong Kong Polytech Univ, Dept Elect Engn, Hong Kong, Hong Kong, Peoples R China
[3] Singapore Polytech Univ, Sch Elect & Elect Engn, Singapore, Singapore
来源：
COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2003, PT 1, PROCEEDINGS | 2003年 / 2667卷
关键词：
genetic algorithms; parallel architectures; parallel computing;
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：
Machine scheduling problems belong to the most difficult deterministic combinatorial optimization problems. Hence, most scheduling problems are NP-hard and it is impossible to find the optimal schedule in reasonable time. In this paper, we consider a flow-shop scheduling problem with multiprocessor tasks. A parallel genetic algorithm using multithreaded programming technique is developed to obtain a quick but good solution to the problem. The performance of the parallel genetic algorithm under various conditions and parameters are studied and presented.
引用
收藏
页码：987 / 997
页数：11
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