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．