Applied Intelligence

, Volume 49, Issue 2, pp 819–836 | Cite as

A hybrid clonal selection algorithm with modified combinatorial recombination and success-history based adaptive mutation for numerical optimization

  • Weiwei Zhang
  • Kui Gao
  • Weizheng ZhangEmail author
  • Xiao Wang
  • Qiuwen Zhang
  • Hua Wang


Artificial immune system is a class of computational intelligence methods drawing inspiration from biological immune system. As one type of popular artificial immune computing model, clonal selection algorithm (CSA) has been widely used for many optimization problems. When dealing with complex optimization problems, such as the characteristics of multimodal, high-dimension, rotational, the traditional CSA often suffers from diversity loss, poor search ability, premature convergence and stagnation. To address the problems, a modified combinatorial recombination is introduced to bring diversity to the population and avoid the premature convergence. Moreover, the success-history based adaptive mutation strategy is introduced to form a success-history based adaptive mutation based clonal selection algorithm to improve the search ability. The mutation operator is also modified and analyzed through experimental comparison. To further improve the precision and cope with the stagnation, the gene knockout strategy is proposed. The proposed algorithm is tested on CEC 2014 benchmarks and compared with state-of-the-art evolutionary algorithms. The experimental results show that MSHCSA is quite competitive.


Immune system Clonal selection algorithm Optimization Mutation Adaptive 



This work is supported by National Natural Science Foundation of China (No. 61403349, 61501405, 41601418), Funding program for key scientific research projects of universities in Henan province (No. 18A210025), Science and technology research key project of basic research projects in education department of Henan province (No.15A520033, No.14B520066), doctoral foundation(No. 2013BSJJ044), and Student science and technology activity project of Zhengzhou university of light industry.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Zhengzhou University of Light IndustryZhengzhouChina

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