直觉模糊集相似度遗传算法求解多目标车间调度问题
Genetic algorithm based on similarity of intuitionistic fuzzy sets for many-objective flow shop scheduling problems
摘要点击 47  全文点击 92  投稿时间:2018-04-03  修订日期:2018-12-27
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DOI编号  10.7641/CTA.2018.80232
  2019,36(7):1057-1066
中文关键词  多目标优化  置换流水车间调度  直觉模糊集相似度  遗传算法  
英文关键词  Many-objective permutation flow-shop scheduling  similarity of intuitionistic fuzzy set  genetic algorithm  
基金项目  工信部2016智能制造综合标准化与新模式应用项目(工信部联装(2016)213号);福建省科技厅科技计划重点项目(No. 2016H0015);福建省高端装备制造协同创新中心项目(2015A003);CAD/CAM福建省高校工程研究中心开放基金(K201704)
学科分类代码  
作者单位E-mail
徐文婕 福州大学 861763958@qq.com 
朱光宇 福州大学 zhugy@fzu.edu.cn 
中文摘要
      为了提高高维多目标置换流水车间调度问题的求解质量,提出基于直觉模糊集相似度的遗传算法(Similarity of Intuitionistic Fuzzy Sets GA , SIFS_GA)。算法中分别将参考解和Pareto解映射为参考解直觉模糊集和Pareto解直觉模糊集。计算两个集合之间的直觉模糊相似度,用以判断Pareto解的优劣。以直觉模糊集相似度值引导多目标遗传算法进化。对6个CEC标准测试集与10个流水车间调度测试实例进行仿真实验,结果表明SIFS_GA算法性能优于常用的多目标优化算法,且可以有效解决多目标置换流水车间调度问题,尤其在解决规模较大的问题上是一种有效方法。
英文摘要
      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 on 6 CEC benchmark examples and 10 flow shop scheduling test examples to analyze the proposed algorithm. Experimental results show that SIFS_GA can an 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.