An improved genetic algorithm with recurrent search for the job-shop scheduling problem

Yingjie Xing, Zhuqing Wang, Jing Sun, Wanlei Wang

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

A genetic algorithm with some improvement is proposed to avoid the local optimum for job-shop scheduling problem (JSP). There is recurrent searching process of genetic operation in the improved genetic algorithm. The improved crossover operation can shake current population from local optimum in genetic algorithm. The recurrent crossover operation and mutation operation can inherit excellent characteristics from parent chromosomes and accelerate the diversity of offspring. Both benchmark FT(6×6) and LA1(10×5) job-shop scheduling problems are used to show the effectiveness of the proposed method. Experimental results demonstrate that the proposed genetic algorithm does not get stuck at a local optimum easily, and it is fast in convergence, simple to be implemented.

本文言語English
ホスト出版物のタイトルProceedings of the World Congress on Intelligent Control and Automation (WCICA)
ページ3386-3390
ページ数5
DOI
出版ステータスPublished - 2006 12 1
外部発表はい
イベント6th World Congress on Intelligent Control and Automation, WCICA 2006 - Dalian, China
継続期間: 2006 6 212006 6 23

出版物シリーズ

名前Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
1

Conference

Conference6th World Congress on Intelligent Control and Automation, WCICA 2006
CountryChina
CityDalian
Period06/6/2106/6/23

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Science Applications

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