基于改进蝙蝠算法的柔性流水车间排产优化问题研究

摘要

In order to solve the flexible flow shop scheduling problem,this paper proposed the SEBA.The existing BA could not solve the discrete problem because it was easily trapped in local extremum and had low accuracy of the optimization results.SEBA adopted the ROV coding method,which made the algorithm suitable for solving discrete FFSP problems.This paper designed the set of the elite individuals based on hamming distance,which had higher fitness and lower similarities.It could also take turns to lead the population evolution,enhance the vitality of population evolution and avoid optimization process trap in local extremum.It designed an adaptive position update method to improve the accuracy of algorithm.Finally,it measured the SEBA by the datas from different scale scheduling benchmark problems with comparison of several algorithms.Simulation results show that SEBA is efficient for solving FFSP.%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.