Genetic algorithm based on similarity of intuitionistic fuzzy sets for many-objective flow shop scheduling problems

被引：1
|
作者：
Xu W.-J. [1 ]
Zhu G.-Y. [1 ]
机构：
[1] School of Mechanical Engineering & Automation, Fuzhou University, Fuzhou, 350116, Fujian
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2019年 / 36卷 / 07期
关键词：
Genetic algorithm; Many-objective optimization; Permutation flow-shop scheduling; Similarity of intuitionistic fuzzy set;
D O I：
10.7641/CTA.2018.80232
中图分类号：
学科分类号：
摘要：
To obtain better solution of many-objective permutation flow-shop scheduling problems (PFSP), a genetic algorithm based on similarity of intuitionistic fuzzy sets (SIFS GA) is proposed. In this algorithm, reference solution and Pareto solution are mapped into reference solution intuitionistic fuzzy sets and Pareto solution intuitionistic fuzzy sets respectively. The similarity of intuitionistic fuzzy sets between two sets is calculated and adopted to determine the quality of the Pareto solution. The similarity value of intuitionistic fuzzy sets is used as the fitness value of GA to guide the algorithm evolution. Finally, simulation experiments are carried out with 6 CEC benchmark examples and 10 flow shop scheduling test examples to analyze the proposed algorithm. Experimental results show that SIFS GA can obtain better results than other commonly used many-objective optimization algorithms, and can effectively solve many-objective flow shop scheduling problems, especially in solving the problem of large scale. © 2019, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
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页码：1057 / 1066
页数：9
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