An improved hybrid immune algorithm for mechanism kinematic chain isomorphism identification in intelligent design

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• 作者：Ping Yang (1)
  Kehan Zeng (2)
  Chunquan Li (1)
  Jianming Yang (3)
  Shuting Wang (4)
  
  1. School of Mechanical Engineering ; Jiangsu University ; Zhenjiang ; 212013 ; People鈥檚 Republic of China
  2. Department of Computer Science ; Huizhou University ; Huizhou ; 516007 ; People鈥檚 Republic of China
  3. Faculty of Engineering and Applied Science ; Memorial University ; St. John鈥檚 ; NL ; A1B 3X5 ; Canada
  4. School of Mechanical Science and Engineering ; Huazhong University of Science and Technology ; Wuhan ; 430074 ; Hubei ; People鈥檚 Republic of China
  
• 关键词：Mechanism kinematic chain isomorphism ; Immune algorithm ; Clonal selection ; Genetic algorithm ; Local search
• 刊名：Soft Computing - A Fusion of Foundations, Methodologies and Applications
• 出版年：2015
• 出版时间：January 2015
• 年：2015
• 卷：19
• 期：1
• 页码：217-223
• 全文大小：839 KB
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• 刊物类别：Engineering
• 刊物主题：Numerical and Computational Methods in Engineering
  Theory of Computation
  Computing Methodologies
  Mathematical Logic and Foundations
  Control Engineering
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1433-7479

文摘

In intelligent mechanism design, isomorphism identification of mechanism kinematic chains (IIMKC) is aimed at avoiding repeated mechanism design and is proved to be an NP-complete problem. In this paper, kinematic chains are represented by graphs. An improved hybrid immune algorithm, which integrates the clonal selection immune algorithm with genetic algorithm and the local search algorithm, is proposed to solve IIMKC problem. Moreover, the novel saving and updating operator is proposed to save the best antibodies and maintain a diverse repertoire of antibodies for improving performance of clonal selection. In addition, the pseudo-crossover operator is introduced to enhance the efficiency of genetic algorithm. Simulation results validate the high efficiency and robustness of the hybrid immune algorithm.