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Virus-Evolutionary Particle Swarm Optimization Algorithm

  • Fang Gao
  • Hongwei Liu
  • Qiang Zhao
  • Gang Cui
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)

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.

Keywords

Particle Swarm Optimization Particle Swarm Business Process Particle Swarm Optimization Algorithm Virtual Enterprise 
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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References

  1. 1.
    Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, vol. 4, pp. 1942–1948 (1995)Google Scholar
  2. 2.
    Eberhart, R., Kennedy, J.: New Optimizer Using Particle Swarm Theory. In: Proc. 6th Int. Symp. Micro Machine Human Science, pp. 39–43 (1995)Google Scholar
  3. 3.
    Yao, X.: Evolutionary Computation: Theory and Applications. World Scientific, Singapore (1999)Google Scholar
  4. 4.
    Tan, K.C., Lim, M.H., Yao, X., Wang, L.P. (eds.): Recent Advances in Simulated Evolution and Learning. World Scientific, Singapore (2004)MATHGoogle Scholar
  5. 5.
    Zhao, Q., Yan, S.Z.: Collision-Free Path Planning for Mobile Robots Using Chaotic Particle Swarm Optimization. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 632–635. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Li, Y.M., Chen, X.: Mobile Robot Navigation Using Particle Swarm Optimization and Adaptive NN. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 628–631. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Silva, A., Neves, A., Costa, E.: An Empirical Comparison of Particle Swarm and Predator Prey Optimisation. In: O’Neill, M., Sutcliffe, R.F.E., Ryan, C., Eaton, M., Griffith, N.J.L. (eds.) AICS 2002. LNCS, vol. 2464, pp. 103–110. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  8. 8.
    Schutte, J.F., Groenword, A.A.: A Study of Global Optimization Using Particle Swarms. J. Global Optimiz. 31, 93–108 (2005)MATHCrossRefGoogle Scholar
  9. 9.
    Ho, S.L., Yang, S.Y., Ni, G.Z., Wong, H.C.: A Particle Swarm Optimization Method with Enhanced Global Search Ability for Design Optimizations of Electromagnetic Devices. IEEE Transations on Magnetics 42, 1107–1110 (2006)CrossRefGoogle Scholar
  10. 10.
    Lu, Z., Hou, Z.: Particle Swarm Optimization with Adaptive Mutation. Acta Electronca Sinica 3, 417–420 (2004)Google Scholar
  11. 11.
    Jiang, C., Etorre, B.: A Self-adaptive Chaotic Particle Swarm Algorithm for Short Term Hydroelectric System Scheduling in Deregulated Environment. Energy Conversion and Management 46, 2689–2696 (2005)CrossRefGoogle Scholar
  12. 12.
    Chatterjee, A., Siarry, P.: Nonlinear Inertia Weight Variation for Dynamic Adaptation in Particle Swarm Optimization. Computers & Operations Research 33, 859–871 (2006)MATHCrossRefGoogle Scholar
  13. 13.
    Kubotan, N., Koji, S., et al.: Role of Virus Infection in Virus-evolutionary Genetic Algorithm. In: Proceedings of the IEEE Conference on Evolutionary Computation, pp. 182–187 (1996)Google Scholar
  14. 14.
    Kubotan, N., Fukuda, T., et al.: Virus-evolutionary Genetic Algorithm for a Self-organizing Manufacturing System. Computers Ind. Engng. 30, 1015–1026 (1996)CrossRefGoogle Scholar
  15. 15.
    Kubotan, N., Fukuda, T., et al.: Trajectory Planning of Cellar Manipulator System Using Virus-Evolutionary Genetic Algorithm. Robotics and Autonomous System 19, 85–94 (1996)CrossRefGoogle Scholar
  16. 16.
    Kubotan, N., Fukuda, T., et al.: Evolutionary Transition of Virus-evolutionary Genetic Algorithm. In: Proceedings of the IEEE Conference on Evolutionary Computation, pp. 291–296 (1997)Google Scholar
  17. 17.
    Kubotan, N., Arakawa, T., et al.: Trajectory Generation for Redundant Manipulator Using Virus Evolutionary Genetic Algorithm. In: Proceedings of the IEEE Conference on Robotics and Automation, pp. 205–210 (1997)Google Scholar
  18. 18.
    Kubotan, N., Fukuda, T.: Schema Representation in Virus-Evolutionary Genetic Algorithm for Knapsack Problem. In: IEEE World Congress on Computational Intelligence – The 1998 IEEE International Conference on Evolutionary Computation Proceedings, pp. 834–839. IEEE, Anchorage (1998)Google Scholar
  19. 19.
    Feng, W.D., Chen, J., Zhao, C.J.: Partners Selection Process and Optimization Model for Virtual Corporations Based on Genetic Algorithms. Journal of Tsinghua University (Science and Technology) 40, 120–124 (2000)Google Scholar
  20. 20.
    Qu, X.L., Sun, L.F.: Implementation of Genetic Algorithm to the Optimal Configuration of Manufacture Resources. Journal of Huaqiao University 26, 93–96 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fang Gao
    • 1
  • Hongwei Liu
    • 1
  • Qiang Zhao
    • 2
  • Gang Cui
    • 1
  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  2. 2.School of TrafficNortheast Forestry UniversityHarbinChina

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