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首页> 外文期刊>International Journal of Production Research >Many-objective flow shop scheduling optimisation with genetic algorithm based on fuzzy sets
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Many-objective flow shop scheduling optimisation with genetic algorithm based on fuzzy sets

机译：基于模糊套的遗传算法多目标流量店调度优化

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摘要

To solve many-objective flow-shop scheduling problems (FSP), a genetic algorithm based on the relative entropy of fuzzy sets (REFS_GA) is proposed. A mathematical model of the many-objective FSP is built, which involves four scheduling criterions of FSP. In REFS_GA, the Pareto front is mapped to fuzzy set, and the relational entropy coefficient of fuzzy sets is used to measure the similarity between the fuzzy sets of Pareto solutions and ideal solution. The coefficient is used as the fitness of genetic algorithm (GA) and to guide algorithm evolution. The performance of REFS_GA is evaluated through compared with GA based on g-dominance (gGA), random weight GA (rwGA) and the third version of non-dominated sorting genetic algorithm (NSGA-III). Experiments are carried out with eight DTLZ benchmark functions, six MaF benchmark functions with 4, 7 or 10 objectives, respectively, nine scheduling problems with four objectives and a real-world many-objective FSP. Experimental results show that REFS_GA can solve may-objective benchmark functions and many-objective FSP. The optimisation solution and performance indicators of REFS_GA are better than gGA, rwGA and NSGA-III. It can be concluded that REFS_GA is an effective method to solve many-objective optimisation problems. The main contributions of the work are that a four-objective model of FSP is built and a priori approach based on fuzzy set is proposed to solve many-objective FSP.
机译：为了解决许多客观流量店调度问题（FSP），提出了一种基于模糊集（REFS_GA）的相对熵的遗传算法。建立了许多客观FSP的数学模型，这涉及FSP的四个调度标准。在REFS_GA中，Pareto Front映射到模糊集，模糊集的关系熵系数用于测量帕累托解决方案模糊套和理想解决方案之间的相似性。系数用作遗传算法（GA）的适应性和指导算法演化。通过基于G-优势（GGA），随机权重GA（RWGA）和非主导分选遗传算法（NSGA-III）的第三种第三种第三种第三种，通过与GA进行比较来评估REFS_GA的性能。实验与八个DTLZ基准函数进行，六个MAF基准功能，分别为4,7或10个目标，九个调度问题，具有四个目标和真实世界的多目标FSP。实验结果表明，REFS_GA可以解决可能的基准功能和多目标FSP。 REFS_GA的优化解决方案和性能指标优于GGA，RWGA和NSGA-III。可以得出结论，Refs_GA是解决多目标优化问题的有效方法。该工作的主要贡献是建立了一个四目标的FSP模型，并提出了一种基于模糊集的先验方法来解决许多客观的FSP。

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