期刊
KNOWLEDGE-BASED SYSTEMS
卷 182, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.knosys.2019.07.011
关键词
Memetic algorithm; Pareto front; Local search; Multi-objective optimization; Job shop scheduling problems
资金
- National Key R&D Program of China [2018YFB1701400]
- National Natural Science Foundation of China [71473077]
- State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, China [71775004]
- State Key Laboratory of Construction Machinery [SKLCM2019-03]
This paper presents an effective memetic algorithm (EMA) to solve the multi-objective job shop scheduling problem. A new hybrid crossover operator is designed to enhance the search ability of the proposed EMA and avoid premature convergence. In addition, a new effective local search approach is proposed and integrated into the EMA to improve the speed of the algorithm and fully exploit the solution space. Experimental results show that our improved EMA is able to easily obtain better solutions than the best-known solutions for about 95% of the tested difficult problem instances that are widely used in the literature, demonstrating its superior performance both in terms of solution quality and computational efficiency. (C) 2019 Elsevier B.V. All rights reserved.
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