基于改进蝙蝠算法的柔性流水车间排产优化问题研究

发布时间：2018-06-27 05:23

本文选题：柔性流水车间问题 + 蝙蝠算法　；参考：《计算机应用研究》2017年07期

【摘要】：为解决柔性流水车间调度问题(flexible flow shop scheduling problem,FFSP),提出了一种基于精英个体集的自适应蝙蝠算法(self-adaptive elite bat algorithm,SEBA)。针对蝙蝠算法存在求解离散问题具有局限性、易陷入局部极值、优化结果精度低等问题,该算法采用ROV(ranked order value)编码方式,使算法适用于求解离散型的FFSP;提出基于汉明距离的精英个体集,由多个适应度高但相似度低的精英个体轮流引导种群进化,增强种群进化活力,避免寻优过程陷入局部极值;提出自适应位置更新机制,提高算法优化精度。最后采用不同规模的标准实例对改进算法进行测试,与已有算法进行对比,实验结果验证了改进蝙蝠算法求解FFSP问题的有效性。
[Abstract]:In order to solve the flexible flow shop scheduling problem (flexible flow shop scheduling problem FFSP), an adaptive bat algorithm based on elite individual set (SEBA) is proposed. In order to solve discrete problems, bat algorithm is easy to fall into local extremum, and the precision of optimization results is low. This algorithm adopts (ranked order value) coding method. The algorithm is suitable for solving discrete FFSPs, and a set of elite individuals based on hamming distance is proposed, in which several elite individuals with high fitness but low similarity take turns to guide population evolution, enhance the vitality of population evolution, and avoid falling into local extremum in the process of optimization. An adaptive position updating mechanism is proposed to improve the optimization accuracy of the algorithm. Finally, the improved algorithm is tested with standard examples of different scales and compared with the existing algorithms. The experimental results show that the improved bat algorithm is effective in solving FFSP problems.
【作者单位】：沈阳建筑大学信息与控制工程学院;中国科学院沈阳自动化研究所数字工厂研究室;中国科学院网络化控制系统重点实验室;
【基金】：国家自然科学基金资助项目(61503259) 辽宁省社会科学规划基金资助项目(L15BGL017) 校涵育项目(XKHY2-61)
【分类号】：TB497;TP18

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