车间调度的非盲自适应Cat群优化

Bo Shi, Ming-Yu Liu
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引用次数: 0

摘要

针对车间调度问题,提出了一种非盲自适应猫群优化算法。将离散序列根据排序值映射为实数编码。采用逆向学习对种群进行初始化。同时,采用非盲自适应策略改进了猫群优化的模式分配规则,增强了搜索和跟踪模式参数的自适应性,保证了全局搜索和局部搜索的同时进行。结果表明,该算法具有较强的鲁棒性,能较好地解决大规模车间调度问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Non-blind Adaptive Cat Swarm Optimization for Workshop Scheduling
A non-blind adaptive cat swarm optimization algorithm was proposed aiming at the workshop scheduling. The discrete sequences were mapped to real coding based on ranked order value. The reverse learning was applied to initialize the population. Also, the non-blind adaptive strategy was adopted to improve the mode assignment rule of cat swarm optimization, enhance the adaptivity of parameters in the seeking and tracking mode in order to ensure both the global search and local search. The results indicated that the non-blind adaptive cat swarm optimization was robust and could get better scheduling solutions in large scale workshop scheduling problem.
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