Title :
An Improved Genetic Algorithm with Recurrent Search for the Job-Shop Scheduling Problem
Author :
Xing, Yingjie ; Wang, Zhuqing ; Sun, Jing ; Wang, Wanlei
Author_Institution :
Key Lab. for Precision & Non-traditional Machining Technol. of Minist. of Educ., Dalian Univ. of Technol.
Abstract :
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(6times6) and LA1(10times5) 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
Keywords :
convergence; genetic algorithms; job shop scheduling; search problems; genetic algorithm; job-shop scheduling problem; mutation operation; parent chromosomes; recurrent crossover operation; recurrent search; recurrent searching process; Biological cells; Educational technology; Electronic mail; Genetic algorithms; Genetic mutations; Job shop scheduling; Laboratories; Machining; Optimization methods; Sun; Crossover operation; Genetic algorithm; Job-shop; Recurrent search;
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
DOI :
10.1109/WCICA.2006.1712996
