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A Phased Adaptive PSO Algorithm for Multimodal Function Optimization

  • Haiping Yu
  • Fengying Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7389)

Abstract

Particle swarm optimization is a powerful algorithm that has been applied to various kinds of problems. However, it suffers from falling into local minimum and prematurity especially on multimodal function optimization problems. In this paper, a phased adaptive particle swarm optimization(PAPSO) is proposed to solve such problem. The process is divided into the initial particle pre-searching phase and the post-searching cooperative phase. In the post phase, the strategy of selecting randomly a certain number of particles for entering the reverse-learning is one of the most effective ways of escaping local stagnation. The illustrative example is provided to confirm the validity, as compared with the SPSO, Dynamic Inertia Weight PSO(PSO-W), and Tradeoff PSO(PSO-T) in terms of convergence speed and the ability of jumping out of the local optimal value. Simulation results confirm that the proposed algorithm is effective and feasible.

Keywords

particle swarm optimization multimodal function adaptive 

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References

  1. 1.
    Eberhart, R., Kennedy, J.: A New Optimizer Using Particle Swarm Theory. In: Proc. 6th Int. Symp. Micromach. Hum. Sci., Nagoya, Japan, pp. 39–43 (1995)Google Scholar
  2. 2.
    Nie, P., Ji, G.Q., Zhi, G.: Self-adaptive Inertia Weight PSO Test Case Generation Algorithm Considering Prematurity Restraining. International Journal of Digital Content Technology and its Applications 5(9), 125–133 (2011)CrossRefGoogle Scholar
  3. 3.
    Chen, F.: Tradeoff Strategy Between Exploration and Exploitation for PSO. In: Seventh International Conference on Natural Computation, pp. 1216–1222 (2011)Google Scholar
  4. 4.
    Abdel, K., Rehab, F.: An Improved Discrete PSO with GA Operators for Qos-multicase Routing. International Journal of Hybrid Information Technology, 223–238 (2011)Google Scholar
  5. 5.
    Wang, X.H., Li, J.J.: Hybrid Particle Swarm Optimization with Simulated Annealing. In: Proceedings of the Third International Conference on Machine Learning and Cybernetics, pp. 26–29 (2004)Google Scholar
  6. 6.
    Li, S.T., Tan, M.K., Ivor, W.T.: A Hybrid PSO-BFGS Strategy for Global Optimization of Multimodal Functions. IEEE Trans. on Systems, Man and Cybernetics 41(4) (2011)Google Scholar
  7. 7.
    Wang, Y.F., Zhang, Y.F.: A PSO-based Multi-objective Optimization Approach to the Integration of Process Planning and Scheduling. In: 8th IEEE International Conference on Control and Automation, pp. 614–619 (2010)Google Scholar
  8. 8.
    Hu, X., Eberhart, R.: Multiobjective Optimization Using Dynamic Neighborhood Particle Swarm Ptimization. In: Congress on Evolutionary Computation (CEC 2002), vol. 2, pp. 1677–1681. IEEE Service Center, Piscataway (2002)Google Scholar
  9. 9.
    Y, S.: Design of Neural Network Gain Scheduling Flight Control Law Using a Modified PSO Algorithm Based on Immune Clone Principle. In: Second International Conference on Intelligent Computation Technology and Automation, pp. 259–263 (2009)Google Scholar
  10. 10.
    Ho, S.Y., Lin, H.S., Liauh, W.H., Ho, S.J.: OPSO Orthogonal Particle Swarm Optimization and its Application to Task Assignment Problems. IEEE Trans. Syst., Man, Cybern. A, Syst. Humans 38(2), 288–298 (2008)CrossRefGoogle Scholar
  11. 11.
    Sotirios, K., Goudos, V.M., Theodoros, S.: Application of a Comprehensive Learning Particle Swarm Optimizer to Unequally Spaced Linear Array Synthesis With Sidelobe Level Suppression and Null Control. IEEE Antennas and Wireless Propagation Letters 9, 125–129 (2010)CrossRefGoogle Scholar
  12. 12.
    Clerc, M., Kennedy, J.: The Particle Swarm-explosion Stability and Convergence in a Multidimensional Complex Space. IEEE Trans. Evol. Comput., 58–73 (2002)Google Scholar
  13. 13.
    Li, X.D.: Niching Without Niching Parameters: Particle Swarm Optimization Using a Ring Topology. IEEE Transactions on Evolutionary Computation, 150–169 (February 2010)Google Scholar
  14. 14.
    Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.A.: Opposition-based Differential Evolution. IEEE Trans. Evolut. Comput. 12, 64–79 (2008)CrossRefGoogle Scholar
  15. 15.
    Wang, H., Zhi, J.W., Shahryar, R.: Enhancing Particle Swarm Optimization Using Generalized Opposition-based Learning. Information Sciences 181 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Haiping Yu
    • 1
  • Fengying Yang
    • 2
  1. 1.Faculty of Information EngineeringCity College Wuhan University of Science and TechnologyWuhanChina
  2. 2.College of Information EngineeringHuanghuai UniversityHenanChina

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