针对复杂机械产品零部件选择装配中个体重复现象，本文提出一种新的解集评价指标：种群均匀度。基于种群均匀度和拥挤度相结合的子代精英保留策略，改进了多目标优化NSGA2(Non-dominated Sorting Genetic Algorithm-2)算法。以装配合格率和装配精度为质量评价指标，建立选择装配多目标优化模型。引进近邻搜索算子，克服NSGA2算法局部搜索能力的不足。以某型号航空活塞发动机装配为例，优化结果以Pareto边界集表示，结果表明算法改进之后非支配解集的多样性和收敛性均得到了提高。

Aiming at the replication phenomenon of individual components and parts in the selective assembly of complex mechanical products,this paper proposes a new evaluation index of solution set:population evenness, an elite reserved strategy of the offspring based on the combination of population evenness and degree of congestion is proposed，in order to improve the multi-objective optimization NSGA2(Non-dominated Sorting Genetic Algorithm-2) algorithm.With assembly qualified rate and assembly precision as the quality evaluation index, a multi-objective optimization model for selective assembly is established. Use the nearest neighbor search operator to overcome the deficiencies of the local search capability of the NSGA2 algorithm. Taking the assembly of a certain type of aircraft piston engines as an example, the optimization result is represented by the Pareto boundary set. The result shows that the diversity and astringency of the non-dominated solution sets (non-dominated solution sets) are obtained after the algorithm is improved.

重庆航天火箭电子技术有限公司装配工艺研究