Virus-Evolutionary Particle Swarm Optimization Algorithm (original) (raw)

Abstract

This paper presents an improved discrete particle swarm optimization algorithm based on virus theory of evolution. Virus-evolutionary discrete particle swarm optimization algorithm is proposed to simulate co-evolution of a particle swarm of candidate solutions and a virus swarm of substring representing schemata. In the co-evolutionary process, the virus propagates partial genetic information in the particle swarm by virus infection operators which enhances the horizontal search ability of particle swarm optimization algorithm. An example of partner selection in virtual enterprise is used to verify the proposed algorithm. Test results show that this algorithm outperforms the discrete PSO algorithm put forward by Kennedy and Eberhart.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, 150001, Harbin, China
    Fang Gao, Hongwei Liu & Gang Cui
  2. School of Traffic, Northeast Forestry University, 150040, Harbin, China
    Qiang Zhao

Authors

  1. Fang Gao
  2. Hongwei Liu
  3. Qiang Zhao
  4. Gang Cui

Editor information

Editors and Affiliations

  1. Life Science Research Center, School of Electronic Engineering, Xidian University, 710071, Xi’an, Shaanxi, China
    Licheng Jiao
  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore
    Lipo Wang
  3. School of Electronic Engineering, Xidian Univ., P.O. Box, 710071, Xi’an, P.R. China
    Xinbo Gao
  4. College of Mathematics and Information Science, Hebei Normal University, 050016, Shijiazhuang, Hebei, P.R. China
    Jing Liu
  5. Multi-Agent Systems Lab,Department of Computer Science, University of Science and Technology of China, 230026, Hefei, China
    Feng Wu

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© 2006 Springer-Verlag Berlin Heidelberg

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Gao, F., Liu, H., Zhao, Q., Cui, G. (2006). Virus-Evolutionary Particle Swarm Optimization Algorithm. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223\_20

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