Solving flexible job shop scheduling problem by a multi-swarm collaborative genetic algorithm

被引：0
|
作者：
WANG Cuiyu [1 ]
LI Yang [1 ]
LI Xinyu [1 ]
机构：
[1] School of Mechanical Science and Engineering, Huazhong University of Science and Technology
来源：
Journal of Systems Engineering and Electronics | 2021年 / 32卷 / 02期
基金：
国家重点研发计划;
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论]; TH165 [柔性制造系统及柔性制造单元];
学科分类号：
080202 ; 081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The flexible job shop scheduling problem(FJSP),which is NP-hard, widely exists in many manufacturing industries. It is very hard to be solved. A multi-swarm collaborative genetic algorithm(MSCGA) based on the collaborative optimization algorithm is proposed for the FJSP. Multi-population structure is used to independently evolve two sub-problems of the FJSP in the MSCGA. Good operators are adopted and designed to ensure this algorithm to achieve a good performance. Some famous FJSP benchmarks are chosen to evaluate the effectiveness of the MSCGA. The adaptability and superiority of the proposed method are demonstrated by comparing with other reported algorithms.
引用
收藏
页码：261 / 271
页数：11
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