面向第二类装配线平衡问题的改进粒子群算法  

Improved particle swarm optimization algorithm for the second kind of assembly-line balancing problem

作　　者：肖晖 郑巧仙 XIAO Hui;ZHENG Qiaoxian(School of Computer Science and Information Engineering,Hubei University,Wuhan 430062,China)

机构地区：[1]湖北大学计算机与信息工程学院,湖北武汉430062

出　　处：《湖北大学学报（自然科学版）》2023年第2期164-170,共7页Journal of Hubei University：Natural Science

基　　金：国家自然科学基金(61803149)资助。

摘　　要：为求解第二类装配线平衡问题,建立一种以最小化节拍、工位负载标准差为优化目标的第二类装线平衡问题的模型.根据装配线平衡问题的特点,设计出一种改进的粒子群算法,引入随迭代次数增加而线性递减的惯性权重,防止粒子群算法陷入局部极值点;将反向学习策略与PSO算法相结合,使PSO算法具有更佳的搜索能力和收敛速度.通过求解标杆问题,结果表明改进的PSO算法与标准PSO算法相比,具备更好的求解能力.最后通过对青贮机装配线为实例验证算法的可行性和有效性,进一步验证了本文中提出的改进PSO算法具有很强的计算效率和求解能力.For the second kind of assembly line balancing problem,an assembly-line balancing model was established to minimize the production beat and the standard deviation of assembly line workstation load.According to the characteristics of assembly line balancing problem,an improved particle swarm optimization(PSO)algorithm was designed.The inertia weight which decreases linearly with the increase of iteration times was introduced to prevent the PSO algorithm from falling into local extreme points.Combining the reverse learning strategy with PSO algorithm,PSO algorithm had better search ability and convergence speed.By solving the benchmark problem,the results show that the improved PSO algorithm has better solving ability than that of the standard PSO algorithm.Finally,the feasibility and effectiveness of the algorithm are verified by taking the silage assembly line as an example,which further verifies that the improved PSO algorithm proposed in this paper has strong computational efficiency and solving ability.

关 键 词：装配线平衡 多目标优化 粒子群算法 局部极值 反向学习 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]