应用改进萤火虫算法求解基于学习退化效应的PFSP问题

发布时间：2018-03-10 00:22

本文选题：改进萤火虫算法　切入点：惯性权重　出处：《系统科学学报》2017年04期 　论文类型：期刊论文

【摘要】：为了提高基于学习退化效应的置换流水车间调度问题的求解效率,提出一种改进的萤火虫算法来增强算法性能。首先,给出一种基于目标函数的动态自适应惯性权重莱维飞行萤火虫算法,提高了算法收敛速度,易于快速搜索局部及全局最优解;其次,在标准萤火虫算法的基础上对每次移动后的萤火虫群引入差分进化算法,促进萤火虫个体决策域半径内的信息交换与共享,增加种群多样性,提升了算法收敛精度;最后,根据机器加工具有学习及退化效应的特性,通过Matlab对Car类和Rec类置换Flow-shop Benchmark问题的测试验证了改进萤火虫算法对于求解此类问题有很好的可行性及鲁棒性,并分析了不同学习率与退化效应因子组合对目标函数的影响。
[Abstract]:In order to improve the efficiency of solving the permutation income job shop scheduling problem based on learning degradation effect, an improved firefly algorithm is proposed to enhance the performance of the algorithm. This paper presents a dynamic adaptive inertial weight Levy flying firefly algorithm based on objective function, which improves the convergence speed of the algorithm and is easy to quickly search the local and global optimal solutions. Secondly, Based on the standard firefly algorithm, the differential evolution algorithm is introduced to the firefly group after each move, which promotes the exchange and sharing of information within the radius of the individual decision domain of the firefly, increases the diversity of the population and improves the convergence accuracy of the algorithm. Finally, According to the characteristics of learning and degeneracy in machine processing, the feasibility and robustness of the improved firefly algorithm for solving this kind of problem are verified by Matlab test of Car class and Rec class permutation Flow-shop Benchmark problem. The effects of the combination of different learning rates and degenerate effect factors on the objective function are analyzed.
【作者单位】：桂林电子科技大学商学院;
【基金】：广西高等学校科学研究重点资助项目(SK13ZD016) 广西研究生教育创新计划资助项目(YCSW2012066,YCSW2015155) 国家大学生创新项目(ZJW41137)
【分类号】：TB497;TP18

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