A Hybrid PSO/GA Algorithm for Job Shop Scheduling Problem

被引：0
|
作者：
Tang, Jianchao [1 ,2 ]
Zhang, Guoji [3 ]
Lin, Binbin [3 ]
Zhang, Bixi [4 ]
机构：
[1] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510641, Guangdong, Peoples R China
[2] South Chain Normal Univ, Comp Sch, Guangzhou 510631, Peoples R China
[3] South China Univ Technol, Coll Sci, Guangzhou 510641, Guangdong, Peoples R China
[4] Guangdong Univ Technol, Sch Econ Management, Guangzhou 510090, Peoples R China
来源：
ADVANCES IN SWARM INTELLIGENCE, PT 1, PROCEEDINGS | 2010年 / 6145卷
基金：
中国国家自然科学基金;
关键词：
particle swarm optimization algorithm; hybrid genetic algorithm; Job shop scheduling problem; SEARCH ALGORITHM; OPTIMIZATION; CONVERGENCE;
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：
The job shop scheduling problem is a well-known NI) hard problem, on which genetic algorithm is widely used However. due to the lack of the major evolution direction. the effectiveness of the regular genetic algorithm is restricted In this paper. we propose a new hybrid genetic algorithm to solve the job shop scheduling problem The particle swarm optimization algorithm is introduced to get the initial population, and evolutionary genetic operations are proposed We validate the new method on seven benchmark datasets. and the comparisons with some existing methods verify as effectiveness
引用
收藏
页码：566 / +
页数：3
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