A Novel Clonal Selection for Multi-modal Function Optimization

  • Meng Hong-yun
  • Zhang Xiao-hua
  • Liu San-yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)


This paper proposes a Clonal Selection Algorithm for Multimodal function optimization (CSAM) based on the concepts of artificial immune system and antibody clonal selection theory. In CSAM, more attention is paid to locate all the peaks (both global and local ones) of multimodal optimization problems. To achieve this purpose, new clonal selection operator is put forward, dynamic population size and clustering radius are also used not only to locate all the peaks as many as possible, but assure no resource wasting, i.e., only one antibody will locate in each peak. Finally, new performances are also presented for multimodal function when there is no prior idea about it in advance. Our experiments demonstrated that CSAM is very effective in dealing with multimodal optimization regardless of global or local peaks.


Particle Swarm Optimization Clonal Selection Artificial Immune System Multimodal Function Cluster Radius 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Meng Hong-yun
    • 1
  • Zhang Xiao-hua
    • 2
  • Liu San-yang
    • 1
  1. 1.Dept.of Applied Math.Xidian UniversityXianChina
  2. 2.Institute of Intelligent Information ProcessingXidian UniversityXianChina

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