基于改进麻雀搜索算法的装配线平衡问题研究OA
Research on assembly line balance problem based on improved sparrow search algorithm
针对第一类装配线平衡问题,并结合第三类装配线平衡问题,提出一种改进麻雀搜索算法.该方法引入精英反向学习策略、混沌映射策略以及混合差分进化策略,可有效改进麻雀搜索算法的全局搜索能力以及种群陷入局部最优的问题.此外,在优化目标方面,在求解最小工位数的基础上增加了装配线平衡率与平滑指数相结合的优化目标.通过求解某公司的相关实际算例验证,结果表明,装配线平衡率从73.57％提升至98.69％,相比最初设计提升了 34.14％,并在多个不同算例下,使用多个不同算法进行对比,进一步验证了该算法对装配线平衡问题具有较好的求解效果.
Aiming at the first type of assembly line balance problem and the third type of assembly line balance problem,an im-proved sparrow search algorithm is proposed.By introducing reverse learning strategy,chaotic mapping strategy and hybrid differ-ential evolution strategy,this method can effectively improve the global search ability of sparrow search algorithm and the problem of local optimal population.In addition,on the basis of solving the minimum number of stations,an optimization objective combi-ning assembly line balance rate and smoothness index is added.The results show that the assembly line balance rate is improved from 73.57％to 98.69％,which is 34.14％higher than the original design.Moreover,it is further verified that the algorithm has a better solving effect on the assembly line balance problem by comparing several different algorithms under several different cal-culation examples.
李知非;刘波;黄鹤军;娄嘉骏
南昌航空大学无损检测技术教育部重点实验室,南昌 330063||宁波水表(集团)股份有限公司,宁波 315032南昌航空大学无损检测技术教育部重点实验室,南昌 330063江西中烟广丰卷烟厂,上饶 334000宁波水表(集团)股份有限公司,宁波 315032
信息技术与安全科学
装配线平衡改进麻雀搜索算法反向学习混沌映射混合差分进化
assembly line balanceimproved sparrow search algorithmchaotic mappingdifferential evolutionmulti-objective op-timization
《现代制造工程》 2026 (2)
1-11,11
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