Fast Multi-objective Hybrid Evolutionary Algorithm for Flow Shop Scheduling Problem
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 evalua
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Fast Multi-objective Hybrid Evolutionary Algorithm for Flow Shop Scheduling Problem Wenqiang Zhang, Jiaming Lu, Hongmei Zhang, Chunxiao Wang and Mitsuo Gen 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. Keywords Flow shop scheduling · Hybrid evolutionary algorithm evaluated genetic algorithm · Multi-objective optimization
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W. Zhang (B) · J. Lu · H. Zhang · C. Wang Henan University of Technology, Zhengzhou, People’s Republic of China e-mail: [email protected] W. Zhang Key Laboratory of Grain Information Processing and Control, Ministry of Education, Zhengzhou, China M. Gen Fuzzy Logic Systems Institute, Fukuoka, Japan M. Gen Tokyo University of Science, Katsushika, Japan © Springer Science+Business Media Singapore 2017 J. Xu et al. (eds.), Proceedings of the Tenth International Conference on Management Science and Engineering Management, Advances in Intelligent Systems and Computing 502, DOI 10.1007/978-981-10-1837-4_33
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33.1 Introduction Scheduling problem is to allocate scarce resources to different tasks at the same time under certain constraints. It is a decision-making process, which aims to optimize one or more objectives [1]. Flow shop scheduling problem (FSP) is a typical example in the job shop scheduling problem and belongs to the NP-hard problem [2]. The purpose of FSP is to find an optimal solution under certain constraints, so as to realize the effective allocation and utilization of resources. In recent years, FSP has been widely studied and discussed. Amin et al. [3] proposed a new mathematical model for the hybrid FSP and calculated the makespan with a new heuristic algorithm. Gao et al. [4] proposed a hybrid heuristic algorithm, reducing the total flow time of the no wait FSP. Athanasios [5] proposed a new hybrid parallel genetic algorithm to solve the FSP, improving the crossover and mutation operator of genetic algorithm. Zhang and Gu used a discrete artificial bee colony algorithm for the FSP with intermediate buffers in order to minimize the maximum completion time [6]. Gu et al. [7] improved adaptive particle swarm optimization algorithm for s
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