Abstract
In this paper, a fast multi-objective hybrid evolutionary algorithm (MOHEA) is proposed to solve the bi-criteria flow shop scheduling problem with the objectives of minimizing makespan and total flow time. The proposed algorithm improves the vector evaluated genetic algorithm (VEGA) by combing a new sampling strategy according to the Pareto dominating and dominated relationship-based fitness function. VEGA is good at searching the edge region of the Pareto front, but it has neglected the central area of the Pareto front, and the new sampling strategy prefers the center region of the Pareto front. The hybrid sampling strategy improves the convergence performance and the distribution performance. Simulation experiments on multi-objective test problems show that, compared with NSGA-II and SPEA2, the fast multi-objective hybrid evolutionary algorithm is better in the two aspects of convergence and distribution, and has obvious advantages in the efficiency.
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Acknowledgments
This research work is supported by the National Natural Science Foundation of China: No. U1304609, the Key Young Teacher Training Program of Henan University of Technology, the Fundamental Research Funds for the Henan Provincial Colleges and Universities: No. 2014YWQQ12, Research Funds for Key Laboratory of Grain Information Processing and Control (Henan University of Technology), Ministry of Education and the Grant-in-Aid for Scientific Research (C) of Japan Society of Promotion of Science (JSPS): No. 15K00357.
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Zhang, W., Lu, J., Zhang, H., Wang, C., Gen, M. (2017). Fast Multi-objective Hybrid Evolutionary Algorithm for Flow Shop Scheduling Problem. In: Xu, J., Hajiyev, A., Nickel, S., Gen, M. (eds) Proceedings of the Tenth International Conference on Management Science and Engineering Management. Advances in Intelligent Systems and Computing, vol 502. Springer, Singapore. https://doi.org/10.1007/978-981-10-1837-4_33
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DOI: https://doi.org/10.1007/978-981-10-1837-4_33
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