Ant Colony Optimization Combined with Local Search Algorithm for Multi-objective Flexible Job Shop Scheduling Problems
This paper proposes a hybrid approach which combined ant colony optimization with local search algorithm for solving multi-objective flexible job shop scheduling problems. In this proposed algorithm, it employs ant colony optimization, which has excellent exploration and information learning abilities, to provide an appropriate initial schedule for the local search algorithm; then these appropriate feasible solutions were refined by the proposed local search algorithm. The optimization performance of this proposed approach has been improved largely by efficaciously integrating ant colony optimization with local search algorithm. The results obtained from the computational study have shown that the proposed algorithm is a feasible and effective approach for the multi-objective flexible job shop scheduling problems.
Li Li Lining Xing Shijun Yao Xiaoxin Zhu
College of Science, Information Engineering University of PLA Zhengzhou,China 450002 College of Information System and Management National University of Defense Technology Changsha,Chin Department of Electronics Information Engineering,HuaQiao University Quanzhou,China 362021
国际会议
南宁
英文
2007-07-20（万方平台首次上网日期，不代表论文的发表时间）