A Tabu Genetic algorithm with Search Area Adaptation for the Job-Shop Scheduling Problem

The job-shop scheduling problem is the important issues to the research of optimal problems. Besides, tabu search is also applied to GA, called TGA for traveling salesman problem (TSP) that has better effectiveness than GA. Thus, this is an interest and important research area for job-shop scheduling problem with TGA. In this paper, we try to discuss this issue. According to the TGA, it maintains diversity through broad-sense incest prevention. Therefore, the solutions can contain theirs diversity and prevent premature convergence. But in JSP problem, the crossover and mutation manners of TGA cannot produce the better solutions than GA. So we modified the crossover and mutation phases of TGA called modified TGA (MTGA). First, the modified crossover search phase uses a threshold (THc) to control the times of crossover for improving the qualities and convergence of solutions. Second, the mutation search phase use two parameters to control the selected points and the times of mutation in order to make the global search wildly and prevent to drop into local minimum more easily. And the experiments results demonstrate the superiority of MTGA in job-shop scheduling problems. Not only balance intensification, but also diversification. Key-Words: Genetic algorithm, Job-shop scheduling problem, Tabu search