Multi-population interactive coevolutionary algorithm for flexible job shop scheduling problems

Authors:
Li-Ning Xing;Ying-Wu Chen;Ke-Wei Yang
Affiliations:
College of Information System and Management, National University of Defense Technology, Changsha, China;College of Information System and Management, National University of Defense Technology, Changsha, China;College of Information System and Management, National University of Defense Technology, Changsha, China
Venue:
Computational Optimization and Applications
Year:
2011

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Abstract

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.