水平旋转式贴片机贴装质量优化控制仿真  

Placement Quality Optimization Control Simulation for Horizontal Rotation Machine

作　　者：林德育 肖曙红[1] 麦智伟 

机构地区：[1]广东工业大学机电工程学院,广东广州510006

出　　处：《计算机仿真》2017年第5期300-304,共5页Computer Simulation

基　　金：广东省科技计划资助项目(2013B010402015)

摘　　要：水平旋转式贴片机的贴装优化模型是相当复杂的,不仅需要反映出实际的贴装过程,而且要根据不同元件数量进行贴装求解。若单纯采用某种智能算法对其进行求解,往往会出现求解速度慢或求解精度低。通过分析遗传算法和蚁群算法的优缺点,提出了一种改进的遗传蚁群算法实现对贴装过程的求解,算法前期采用遗传算法求得的初始信息素供蚁群算法使用,加快了算法的求解速度;后期改进蚁群算法的信息素更新策略、选择转移策略、自适应调整启发式因子和期望启发式因子,改进后的蚁群算法更好地反映出贴片机的实际贴装情况,有效地避免陷入局部最优,提高算法的收敛精度。仿真结果表明,改进算法大大地缩短了搜索时间,质量精度明显提高,可实现高效率的贴装。By analyzing the advantages and disadvantages of GA and ACA, an improved genetic ant colony algo- rithm for the SMT process is put forward. The initial pheromone is obtained based on the GA to be prepared for ACA, the solving speed is accelerated. Through the update strategy and the transfer strategy of ACA, the heuristic factor and the expectations heuristic factor are adaptively adjusted. The improved ant colony algorithm can better reflect the actual situation of the placement machine, effectively avoid to fall into the local optimum, and improve the conver- gence precision of the algorithm. The simulation results show that the improved algorithm can greatly shorten the search time and obviously improve the convergence precision, which can realize high efficiency of SMT.

关 键 词：遗传蚁群算法 信息素更新策略 选择转移策略 启发式因子 期望启发式因子 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]