基于深度邻域搜索PSO 算法的装配序列优化问题

针对产品结构特征建立几何约束矩阵, 以最大化满足几何约束条件装配次数和最小化装配方向改变次数为目标, 研究产品装配序列优化问题. 利用值变换的粒子位置和速度更新规则, 基于具有随机性启发式算法产生初始种群, 提出一种带有深度邻域搜索改进策略的粒子群算法解决装配序列问题. 通过装配实例验证了所提出算法的性能并对装配序列质量进行了评价, 所得结果表明了该算法在解决装配序列优化问题上的有效性与稳定性.

Assembly sequence planning problem based on particle swarm optimization algorithm with depth local search

For the structure characters of the products, a matrix of geometric constraints is established. An optimization model of assembly sequence planning is studied to maximize the number of assembly operations with the geometric assembly constraints and minimize the number of changing assembly direction. The updating rules of particle position and velocity are derived based on the value transformation. A heuristic algorithm with randomness characteristic is provided in order to generate the initial populations. A particle swarm optimization algorithm with the depth local search strategy is developed to solve the assembly sequence planning problem. To evaluate the performance of the algorithm and the quality of the assembly sequences, an experiment of an assembly instance is tested. The results show the effectiveness and stability of the proposed algorithm on solving the assembly sequence planning problem.