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An Improved Genetic Algorithm Approach on Mechanism Kinematic Structure Enumeration with Intelligent Manufacturing

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Abstract

During the process of mechanism kinematic structure enumeration, isomorphism identification of graphs is an important and complicated problem. The problem is known to be a NP-complete problem. In this paper, according to the mechanism kinematic chain isomorphism identification criteria, a highly efficient hybrid genetic algorithm model is proposed for isomorphism identification. The model method is coupled with genetic algorithm, optimal choice, and optimal crossover operation. It shows a quick convergence rate of the late operation and can avoid convergence to local optimum. Simulation results show that the hybrid algorithm is more rapid and effective compared with simple genetic algorithm and the improved neural network algorithm.

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Acknowledgments

The authors would like to acknowledge the support of the Six Talent Peaks Project of Jiangsu Province (JXQC-006), the support of A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, the support of China Postdoctoral Science Special Foundation (2014T70476), and the support of Innovative Science Foundation for Graduate Students of Jiangsu Province (KYLX15_1054, CXZZ13_0655, CXLX12_0622) during the course of this work.

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Authors and Affiliations

  1. School of Mechanical Engineering, Jiangsu University, Zhenjiang, 212013, People’s Republic of China

    Hao Liu, Shunyi Shi & Ping Yang

  2. Faculty of Engineering and Applied Science, Memorial University, St. John’s, NL, A1B 3X5, Canada

    Jianming Yang

Authors
  1. Hao Liu
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  2. Shunyi Shi
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  3. Ping Yang
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  4. Jianming Yang
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Correspondence to Ping Yang.

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Liu, H., Shi, S., Yang, P. et al. An Improved Genetic Algorithm Approach on Mechanism Kinematic Structure Enumeration with Intelligent Manufacturing. J Intell Robot Syst 89, 343–350 (2018). https://doi.org/10.1007/s10846-017-0564-z

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  • DOI: https://doi.org/10.1007/s10846-017-0564-z

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