摘要： 在工业生产线上,由于零部件放置位置具有随机性,传统的遗传算法很难得到一个较优的分拣方案。为此,受生物共生进化策略的启发,设计一种采用共生进化算法求解柔性分拣作业最优路径的方法。为避免陷入局部最优值,并解决最优解受初始种群影响的问题,采用灾变策略,设置阈值T。若连续T次进化都没有获得更优解则启动灾变,重新产生共生种群,但继承灾变前最优共生体中子个体之间的共生关系。在保留原有种群共生体最优信息的基础上,获得一些全局性的有效信息。实验结果表明,引入灾变策略后的共生进化算法比层级分析法与未引入灾变策略的共生进化算法具有更快的收敛速度,对给定的复杂分拣装配作业能得到更短的路径。

关键词: 分拣作业, 单机调度, 共生进化, 灾变, 柔性

Abstract: In the industrial production line,due to the randomness of component placement,the traditional genetic algorithm is difficult to get a good sorting scheme.This paper puts forward a framework of Cataclysm Symbiotic Evolutionary Algorithm (CSEA) based on the idea of biological symbiosis evolution strategy to solve the problem of optimizing assembly paths.The algorithm adopts the strategy of disaster to avoid premature convergence and solve the problem that the optimal solution is affected by the initial population.It sets the threshold T at first.If the algorithm does not get a more optimal solution after T times of consecutive evolution,it reckons and produces the symbiotic population again.But,the new population’s topological structure inherits from the last population.As a result,it can obtain some global information efficiently on the basis of the optimal information from the original population.Experimental results indicate that,compared with the hierarchial approach and simple symbiotic evolutionary algorithm,the new algorithm efficiently improves the rate of convergence and can get a shorter path for one specific sorting assembly operation.

Key words: sorting operation, single machine scheduling, symbiotic evolution, catastrophe, flexibility

中图分类号: 

• TP399