引用本文: | 陈铁梅,罗家祥,胡跃明.基于蚁群–混合蛙跳算法的贴片机贴装顺序优化[J].控制理论与应用,2011,28(12):1813~1820.[点击复制] |
CHEN Tie-mei,LUO Jia-xiang,HU Yue-ming.Mounting sequence optimization on surface mounting machine using ant-colony algorithm and shuffled frog-leaping algorithm[J].Control Theory and Technology,2011,28(12):1813~1820.[点击复制] |
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基于蚁群–混合蛙跳算法的贴片机贴装顺序优化 |
Mounting sequence optimization on surface mounting machine using ant-colony algorithm and shuffled frog-leaping algorithm |
摘要点击 2644 全文点击 1706 投稿时间:2010-07-30 修订日期:2011-05-16 |
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DOI编号 10.7641/j.issn.1000-8152.2011.12.CCTA100878 |
2011,28(12):1813-1820 |
中文关键词 贴装顺序优化 蚁群算法 混合蛙跳算法 |
英文关键词 component mounting sequence optimization ant-colony algorithm shuffled frog-leaping algorith |
基金项目 国家自然科学基金资助项目(60835001, 60804053, 61105081); 教育部重点科研基金资助项目(200805611065); 广东省科技攻关重大资助项目(912220500017). |
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中文摘要 |
针对喂料器的位置确定的条件下, 研究拱架式贴片机的元器件贴装顺序优化问题. 建立了新的拱架式贴片机贴装顺序的数学模型. 针对问题的路径寻优特点, 把混合蛙跳算法与蚁群算法相融合, 实现对贴片机的元件贴装顺序优化问题的求解. 在算法中提出了适应于贴片机实际贴装情况的分段启发函数、分段信息素以及信息素的分段更新策略等多种改进方法. 为验证算法有效性, 以20块实际生产的PCB为实例进行了测试. 实验结果表明, 算法具有较好的求解精度和全局搜索能力, 与文献中的单一混合蛙跳算法相比, 平均效率提高了7.89%; 与蚁群算法相比, 平均效率提高了3.79%. |
英文摘要 |
The component mounting sequence optimization of the surface mounting machine is considered under the condition that the feeder allocations are known. A new mathematical model of mounting sequence is specifically built for arch surface mounting machines. Based on the characteristics of searching for the optimal path, a new hybrid algorithm of ant-colony algorithm merged with the shuffled frog-leaping algorithm is proposed to solve the problem. According to the actual mounting situation, a few improved methods are proposed in the algorithm, such as the segmented heuristic function, the segmented pheromone, and the pheromone update strategy. To verify the efficiency of the algorithm, component mounting experiments of 20 printed-circuit-boards(PCBs) are tested. The results show the algorithm has higher accuracy in solving the problem, and in searching the optimal path. It provides an improvement in average efficiency 7.89% over the single shuffled frog-leaping algorithm, and 3.79% over the single ant-colony algorithm. |
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