- Title
- An effective memetic algorithm for multi-objective job-shop scheduling
- Creator
- Gong, Guiliang; Deng, Qianwang; Chiong, Raymond; Gong, Xuran; Huang, Hezhiyuan
- Relation
- Knowledge-Based Systems Vol. 182, Issue 15 October 2019, no. 104840
- Publisher Link
- http://dx.doi.org/10.1016/j.knosys.2019.07.011
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2019
- Description
- 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.
- Subject
- memetic algorithm; pareto front; local search; multi-objective optimization; job shop scheduling problems
- Identifier
- http://hdl.handle.net/1959.13/1412791
- Identifier
- uon:36536
- Identifier
- ISSN:0950-7051
- Rights
- © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
- Language
- eng
- Full Text
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