基于粒子群算法的多质量特性下的选装优化方法研究

Optimization of Selective Assembly with Multiple Quality Characteristics Based on MOPSO

提出了一种基于粒子群算法的分组选装的优化方法。针对复杂产品分组装配中多质量特性的特点,构建了以装配间隙波动最小化为目标的优化模型。利用引入共享机制和动态归档机制的多目标粒子群算法对该模型进行求解。最后,通过发动机的活塞和气缸的选择装配实例对所提出方法进行了验证,结果表明,采用该方法可以有效提高产品的装配精度。

This paper presented a grouping method based on MOPSO for selective assembly. Firstly the properties and quality criteria of selective assembly of components with multi-quality characteristics were analyzed to establish a mathematical model. The objective function of this model was to minimize the clearance variation in selective assembly. Fitness sharing strategy and dynamic archiving strategy were introduced to improve MOPSO what was used to solve the model. Finally, the model was applied to optimize the selective assembly problem of an engine. The results show that the method can improve the accuracy of product assembly effectively.