Hybrid evolutionary algorithm with sequence difference-based differential evolution for multi-objective fuzzy flow-shop scheduling problem

被引：0
作者：
Zhang W. [1 ,2 ]
Li C. [1 ]
Yang W. [2 ]
Gen M. [3 ]
机构：
[1] College of Information Science and Engineering, Henan University of Technology, Zhengzhou
[2] Henan Key Laboratory of Grain Photoelectric Detection and Control, Henan University of Technology, Zhengzhou
[3] Fuzzy Logic Systems Institute, Tokyo University of Science, Tokyo
来源：
International Journal of Internet Manufacturing and Services | 2022年 / 8卷 / 04期
基金：
日本学术振兴会; 中国国家自然科学基金;
关键词：
FFSP; fuzzy flow-shop scheduling problem; HEA; hybrid evolutionary algorithm; Pareto front; SDDE; sequence difference-based differential evolution;
D O I：
10.1504/IJIMS.2022.10047680
中图分类号：
学科分类号：
摘要：
In the actual production process of a factory, there are often many uncertain factors, and researchers usually use fuzzy time to express this uncertainty. In this regard, a hybrid evolutionary algorithm with sequence difference-based differential evolution (HEA-SDDE) is proposed to solve fuzzy flow-shop scheduling problem (FFSP). Firstly, the algorithm uses a hybrid sampling strategy based a multi-objective evolutionary algorithm to guide the population to quickly converge to multiple areas of the Pareto front (PF). Secondly, the proposed algorithm applies a sequence difference-based differential evolution (SDDE) strategy, which uses exchanging sequences to determine the sequence differences between individuals, thereby improving the poorly performing individuals in the population. The experiment compares HEA-SDDE with multiple algorithms on 12 problems of different scales for the multi-objective fuzzy flow-shop scheduling problem (MoFFSP). The results demonstrate that the proposed HEA-SDDE has good convergence and distribution performance. Copyright © 2022 Inderscience Enterprises Ltd.
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收藏
页码：308 / 329
页数：21
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