期刊
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
卷 48, 期 1, 页码 139-155出版社
SPRINGER
DOI: 10.1007/s10589-009-9244-7
关键词
Combinatorial optimization; Flexible job shop scheduling; Genetic algorithm; Ant colony optimization; Multi-population; Interactive; Coevolutionary
资金
- National Natural Science Foundation of China [70272002]
- University of China Scholarship Council
- Innovative Foundation for Excellent Graduate Student of National University of Defense Technology
In this paper, it proposes a multi-population interactive coevolutionary algorithm for the flexible job shop scheduling problems. In the proposed algorithm, both the ant colony optimization and genetic algorithm with different configurations were applied to evolve each population independently. By the interaction, competition and sharing mechanism among populations, the computing resource is utilized more efficiently, and the quality of populations is improved effectively. The performance of our proposed approach was evaluated by a lot of benchmark instances taken from literature. The experimental results have shown that the proposed algorithm is a feasible and effective approach for the flexible job shop scheduling problem.
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