摘要
弧焊机器人在实际生产过程中可以提高生产效率.文中对弧焊机器人焊接过程中的路径长度与能耗进行优化,由此提出了一种基于事件触发的自适应邻域离散多目标优化算法(DMOEA/D-ET).该算法以更新粒子比率作为事件触发机制,通过事件触发机制协调全局搜索与局部搜索.采用网格法进行全局搜索,使用基于分解的多目标进化算法(MOEA/D)进行局部搜索,并通过采用自适应邻域策略改善MOEA/D算法解的分布不均问题.通过和其他5个算法在3个TSPLIB问题上进行测试对比,发现所提出的算法具有较好的性能.最后对平衡梁模型的焊接过程进行多目标优化,并与其他5个多目标算法对比,结果表明文中提出的算法得到的优化结果更贴近真实前沿面,解的分布更好.
The arc welding robot can improve the production efficiency in the actual production process.The path length and energy consumption during the welding process were optimized,and an adaptive neighborhood discrete multi-objective optimization algorithm based on event triggering(DMOEA/D-ET)was proposed.The algorithm uses the updated particle ratio as the event trigger mechanism to coordinate the global search and the local search through the event trigger mechanism.The grid method was used for global search and MOEA/D for local search.The problem of uneven distribution of MOEA/D algorithm was improved by adopting adaptive neighborhood stra- tegy .By comparing with the other five algorithms on three TSPLIB problems,the experimental results show that the proposed algorithm has better performance.Finally,multi-objective optimization was carried out for the wel- ding process of the balanced beam model.Compared with the other five multi-objective algorithms,the results show that the proposed algorithm is closer to the real frontier and has better distribution of the solution.
作者
王学武
夏泽龙
顾幸生
WANG Xuewu;XIA Zelong;GU Xingsheng(School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China)
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2019年第4期99-106,共8页
Journal of South China University of Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(61773165,61573144)~~
关键词
弧焊机器人
多目标
路径优化
能耗
自适应邻域策略
arc welding robot
multi-objective
path optimization
energy consumption
adaptive neighborhood strategy
