An improved immune algorithm and its evaluation of optimization efficiency

  • Authors:

  • Chengzhi Zhu;Bo Zhao;Bin Ye;Yijia Cao

  • Affiliations:

  • College of Electrical Engineering, National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, Zhejiang, China;College of Electrical Engineering, National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, Zhejiang, China;College of Electrical Engineering, National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, Zhejiang, China;College of Electrical Engineering, National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, Zhejiang, China

  • Venue:
  • ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
  • Year:
  • 2005

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Abstract

Based on clonal selection principle, an improved immune algorithm (IIA) is proposed in this paper. This algorithm generates the next population under the guidance of the previous superior antibodies (Ab's) in a small and a large neighborhood respectively, in order to realize the parallel global and local search capabilities. The computational results show that higher quality solutions are obtained in a shorter time, and the degree of diversity in population are maintained by the proposed method. Meanwhile, “Average truncated generations” and “Distribution entropy of truncated generations” are used to evaluate the optimization efficiency of IIA. The comparison with clonal selection algorithm (CSA) demonstrates the superiority of the proposed algorithm IIA.