An improved genetic algorithm for Job-shop scheduling problem

被引：0

作者：

Lou Xiao-fang ^{[1
]}

Zou Feng-xing ^{[1
]}

Gao Zheng ^{[1
]}

Zeng Ling-li ^{[1
,2
]}

Ou Wei ^{[2
]}

机构：

[1] Natl Univ Def Technol, Coll Mechatron & Automat, Dept Automat Control, Changsha 410073, Hunan, Peoples R China

[2] Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Peoples R China

来源：

CCDC 2009: 21ST CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-6, PROCEEDINGS | 2009年

关键词：

production scheduling; Job-shop scheduling; genetic algorithm; The strategy reserve the best individual;

D O I：

10.1109/CCDC.2009.5194839

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Because selection, crossover, mutation were all random, they might destroy the present individual which had the best fitness, then impacted run efficiency and converge. So used the strategy reserve the best individual, then the average fitness of chromosomes was improved, and the loss of the best solution was prevented. At the same time introduced the probability of crossover and mutation based on fitness, then it enhanced the genetic algorithm's evolution ability, and the speed of the evolution was increased. And we find it is effective when solve the Job-shop scheduling problem.

引用

页码：2595 / +

页数：3