计算机集成制造系统 ›› 2014, Vol. 20 ›› Issue (12): 2991-2999.DOI: 10.13196/j.cims.2014.12.009

• 产品创新开发技术 • 上一篇    下一篇

基于混合蛙跳算法的复杂产品装配序列规划

王松1,2,孙振忠1,郭建文1,张智聪1   

  1. 1.东莞理工学院机械工程学院
    2.华南理工大学机械与汽车工程学院
  • 出版日期:2014-12-31 发布日期:2014-12-31
  • 基金资助:
    国家自然科学基金资助项目(71201026);广东省教育厅科技创新项目(No.2013KJCX0179);东莞市社会科技发展计划项目(2013108101011);中国散裂中子源机电技术研发联合实验室资助项目(ZD120512)。

Assembly sequence planning based on shuffled frog leaping algorithm

  • Online:2014-12-31 Published:2014-12-31
  • Supported by:
    Project supported by the National Natural Science Foundation,China(No.71201026),the Project of Department of Education of Guangdong Province(No.2013KJCX0179),the Dongguan Social Science and Technology Development Project(No.2013108101011),and the China Spallation Neutron Source Joint Laboratory Electromechanical Technology Development Program,China(No.ZD120512).

摘要: 为提高机械产品的装配效率,提出一种基于混合蛙跳算法的产品装配序列规划方法。该方法针对混合蛙跳算法中各个模因组内的最优样本容易出现趋同性的现象,引入遗传算法,提出最优样本的差异性控制策略,以改善种群的差异性。建立了以装配操作稳定性、惩罚函数、装配方向改变次数和装配工具改变次数为装配序列评价指标的适应度函数模型。以一个装配体实例分析该算法的特性,验证了改进混合蛙跳算法的可行性和稳定性,并将该算法与标准混合蛙跳算法和遗传算法相比较,证明了改进混合蛙跳算法更有效。

关键词: 装配序列规划, 混合蛙跳算法, 遗传算法, 虚拟装配

Abstract: To improve the efficiency of mechanical product assembly,a product assembly sequence planning method based on Shuffled Frog Leaping Algorithm(SFLA)was proposed.In the shuffling process of SFLA,a diversity control strategy was presented for the local best solution in each meme plex by introducing the genetic algorithm,and the diversity of population was improved.The fitness function model was established by taking the stability of sub-assembly,the penalty function,the frequency of assembly direction changes and the frequency of assembly tool changes into account.In an instance of assembly experiment,the feasibility and stability of the modified SFLA was improved.At the same time,the efficiency and ability to find the global best assembly sequence of modified SFLA was compared with standard SFLA and genetic algorithm.The results of experiment showed that the modified SFLA was more efficient.

Key words: assembly sequence planning, shuffled frog leaping algorithm, genetic algorithms, virtual assembly

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