A Modified Particle Swarm Optimization with Dynamic Particles Re-initialization Period

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 265)

Abstract

The particle swarm optimization (PSO) is an algorithm that attempts to search for better solution in the solution space by attracting particles to converge toward a particle with the best fitness. PSO is typically troubled with the problems of trapping in local optimum and premature convergence. In order to overcome both problems, we propose an improved PSO algorithm that can re-initialize particles dynamically when swarm traps in local optimum. Moreover, the particle re-initialization period can be adjusted to solve the problem appropriately. The proposed technique is tested on benchmark functions and gives more satisfied search results in comparison with PSOs for the benchmark functions.

Keywords

Particle Swarm Optimization Particles Re-initialization Mutation operator Multi-start Particles 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  2. 2.
    Kennedy, J., Eberhart, R.C.: A New Optimizer Using Particle Swarm Theory. In: Proceedings of the 6th International Symposium on Micro Machine and Human Science, pp. 39–43 (1995)Google Scholar
  3. 3.
    Parsopoulos, K.E., Plagianakos, V.P., Magoulas, G.D., Vrahatis, M.N.: Objective function stretching to alleviate convergence to local minima. In: Nonlinear Analysis TMA, vol. 47, pp. 3419–3424 (2003)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Eberhart, R.C., Shi, Y.: comparison between genetic algorithms and particle swarm optimization. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 611–616. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  5. 5.
    Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann, San Mateo (2001)Google Scholar
  6. 6.
    Pappala, V.S., Erlich, I., Rohrig, K., Dobschinski, J.A.: A stochastic modal for the optimal operation of a wind–thermal power system. IEEE Trans. Power Syst., 940–950 (2009)CrossRefGoogle Scholar
  7. 7.
    Acharjee, P., Goswamj, S.K.: A decoupled power flow algorithm using particle swarm optimization technique. Energy Convers. Manage, 2351–2360 (2009)CrossRefGoogle Scholar
  8. 8.
    Dutta, S., Singh, S.P.: Optimal rescheduling of generators for congestion management based on particle swarm optimization. IEEE Trans. Power Syst. (2008)Google Scholar
  9. 9.
    Kiranyaz, S., Ince, T., Yildirim, A., Gabbouj, M.: Evolutionary artificial neural networks by multi-dimensional particle swarm optimization. Neural Networks, 1448–1462 (2009)CrossRefGoogle Scholar
  10. 10.
    Wei, H.L., Billings, S.A., Zhao, Y.F., Guo, L.Z.: Lattice dynamical wavelet neural networks implemented using particle swarm optimization for spatio-temporal system identification. IEEE Trans. Neural Network, 181–185 (2009)Google Scholar
  11. 11.
    Lin, C.J., Chen, C.H., Lin, C.T.: A hybrid of cooperative particle swarm optimization and cultural algorithm for neural fuzzy networks and its prediction applications. IEEE Trans. Syst. Man Cybernetics Part C, 55–68 (2009)Google Scholar
  12. 12.
    Zhao, L., Qian, F., Yang, Y., Zeng, Y., Su, H.: Automatically extracting T-S fuzzy models using cooperative random learning particle swarm optimization. Appl. Soft Comput., 938–944 (2010)CrossRefGoogle Scholar
  13. 13.
    Van Den Bergh, F.: An Analysis of Particle Swarm Optimizers. PhD thesis, Department of Computer Science, University of Pretoria, South Africa (2002)Google Scholar
  14. 14.
    Li, N., Suan, D., Cen, Y., Zou, T.: Particle swarm optimization with mutation operator. Computer Engineering and Applications l7, 12–14 (2004)Google Scholar
  15. 15.
    Liu, X., Wang, Q., Liu, H., Li, L.: Particle Swarm Optimization with Dynamic Inertia Weight and Mutation. In: Third International Conference on Genetic and Evolutionary Computing, pp. 620–623 (2009)Google Scholar
  16. 16.
    Chiabwoot, R., Boontee, K.: Mutation Period Calculation for Particle Swarm Optimization. In: 1st International Symposium on Technology for Sustainability, pp. 213–216 (2011)Google Scholar
  17. 17.
    Andrews, P.S.: An investigation into mutation operators for particle swarm optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1044–1051. IEEE, Vancouver (2006)Google Scholar
  18. 18.
    Lin, M., Hua, Z.: Improved PSO Algorithm with Adaptive Inertia Weight and Mutation. In: 2009 World Congress on Computer Science and Information Engineering, pp. 622–625 (2009)Google Scholar
  19. 19.
    Higashi, N., Iba, H.: Particle swarm optimization with Gaussian mutation. In: Proc. of the 2003 IEEE Swarm Intelligence Symphosium, pp. 72–79 (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Chiabwoot Ratanavilisagul
    • 1
  • Boontee Kruatrachue
    • 1
  1. 1.Department of Computer Engineering, Faculty of EngineeringKing Mongkut’s Institute of Technology LadkrabangBangkokThailand

Personalised recommendations