基于Bagging集成聚类的改进遗传算法在装配线平衡中的应用

摘要

针对装配线平衡优化问题中传统遗传算法搜索深度不足的问题,提出一种基于Bagging集成聚类的改进遗传算法,用于平衡优化.通过Bagging对几个K均值算法基学习器进行集成学习,建立一种基于Bagging集成聚类算法的种群聚类分析方法,然后建立双目标装配线平衡优化模型,利用种群聚类分析方法来改进遗传算法的交叉环节,以提高搜索深度.在实例中验证了改进遗传算法在求解双目标装配线平衡问题中的有效性和搜索性能.%Aiming at the shortage of traditional genetic algorithm in the assembly line balancing optimization problem,a new improved genetic algorithm based on Bagging ensemble clustering was proposed for balancing optimization.Through the Bagging several base learners of K-means algorithm were integrated learning,and a population clustering analysis method based on Bagging ensemble clustering was established.Then a mathematical model of double objective assembly line balancing optimization was established.Through this population clustering analysis method,the cross links of genetic algorithm were improved to improve search depth.The effectiveness and search performance of the improved genetic algorithm in solving the problem of double objective assembly line balancing problem were verified in a numerical example.