A Hybrid Optimization Algorithm based on Clonal Selection Principle and Particle Swarm Intelligence

  • Authors:

  • Qiaoling Wang;Changhong Wang;X. Z. Gao

  • Affiliations:

  • Harbin Institute of Technology, China;Harbin Institute of Technology, China;University of Technology, Finland

  • Venue:
  • ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
  • Year:
  • 2006

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Abstract

This paperfirst discusses the background knowledge of the clonal selection algorithm and particle swarm method. The clonal selection algorithm is imitated by the basic principle of the adaptive immune response to virus stimulus. The particle swarm optimization is motivated by the social behaviors of swarms. Inspired by these two optimization methods, we propose a hybrid optimization algorithm in this paper. The steps of this hybrid optimization algorithm are described in details, and its performance is evaluated by a unidimensional function optimization and three multidimensional functions optimization problems. It is also compared with both the clonal selection algorithm and particle swarm method based on numerical simulations.