A new immune genetic algorithm for job shop scheduling problem

作者：Ren Qing Dao Er Ji; Wang Yuping^*

来源：Journal of Computational Information Systems, 2013, 9(3): 1011-1018.

• Algorithm convergence
• Bench-mark problems
• Convergence speed
• Crossover and mutation
• Immune genetic algorithms
• Immune operator
• Job shop scheduling problems
• Local characteristics

Job shop scheduling problem is a typical NP-hard problem. To solve the job shop scheduling problem more effectively, specifically designed crossover and mutation operators based on the characteristic of the job shop scheduling problem itself are used in this paper. In order to improve the convergence speed of the algorithm, we utilize the local characteristic information of the problem and design a new immune operator based on the current best solution. Based on these, a new immune genetic algorithm is proposed. The theoretical analysis shows that the proposed algorithm convergence to the optimal solution with probability 1. The computer simulations are made on a set of benchmark problems and the results show the effectiveness of the proposed algorithm.

• 出版日期2013
• 单位西安电子科技大学