Parallel Hybrid Particle Swarm Optimization and Applications in Geotechnical Engineering

  • Youliang Zhang
  • Domenico Gallipoli
  • Charles Augarde
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5821)

Abstract

A novel parallel hybrid particle swarm optimization algorithm named hmPSO is presented. The new algorithm combines particle swarm optimization (PSO) with a local search method which aims to accelerate the rate of convergence. The PSO provides initial guesses to the local search method and the local search accelerates PSO with its solutions. The hybrid global optimization algorithm adjusts its searching space through the local search results. Parallelization is based on the client-server model, which is ideal for asynchronous distributed computations. The server, the center of data exchange, manages requests and coordinates the time-consuming objective function computations undertaken by individual clients which locate in separate processors. A case study in geotechnical engineering demonstrates the effectiveness and efficiency of the proposed algorithm.

Keywords

particle swarm optimization asynchronous parallel computation server-client model hmPSO 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE, NeuralNetworks Council Staff, IEEE Neural Networks Council (eds.) Proc. IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE, Los Alamitos (1995)Google Scholar
  2. 2.
    Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya Japan, pp. 39–43 (1995)Google Scholar
  3. 3.
    Xie, X., Zhang, W., Yang, Z.: A dissipative particle swarm optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), Hawaii, USA, pp. 1456–1461 (2002)Google Scholar
  4. 4.
    Zhang, W., Liu, M., Clerc, Y.: An adaptive pso algorithm for reactive power optimization. In: Sixth international conference on advances in power system control, operation and management (APSCOM) Hong Kong, China, pp. 302–307 (2003)Google Scholar
  5. 5.
    Renders, J., Flasse, S.: Hybrid methods using genetic algorithms for global optimization. IEEE Trans. Syst. Man Cybern. B Cybern. 26(2), 243–258 (1996)CrossRefGoogle Scholar
  6. 6.
    Yen, R., Liao, J., Lee, B., Randolph, D.: A hybrid approach to modeling metabolic systems using a genetic algorithm and Simplex method. IEEE Transactions on Systems, Man and Cybernetics Part-B 28(2), 173–191 (1998)CrossRefGoogle Scholar
  7. 7.
    Fan, S., Liang, Y., Zahara, E.: Hybrid simplex search and particle swarm optimization for the global optimization of multimodal functions. Engineering Optimization 36, 401–418 (2004)CrossRefGoogle Scholar
  8. 8.
    Zhang, Y., Gallipoli, D., Augarde, C.E.: Simulation-based calibration of geotechnical parameters using parallel hybrid moving boundary particle swarm optimization. Computers and Geotechnics 36(4), 604–615 (2009)CrossRefGoogle Scholar
  9. 9.
    Snir, M., Otto, S., Huss-Lederman, S., Walker, D., Dongarra, J.: MPI: The Complete Reference. MIT Press, Cambridge (1996)Google Scholar
  10. 10.
    Nelder, J., Mead, R.: A simplex method for function minimization. The Computer Journal 7, 308–313 (1965)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Alonso, E.E., Gens, A., Josa, A.: A constitutive model for partially saturated soils. Géotechnique 40(3), 405–430 (1990)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Youliang Zhang
    • 1
  • Domenico Gallipoli
    • 2
  • Charles Augarde
    • 3
  1. 1.State Key Laboratory for GeoMechanics and Deep Underground EngineeringChina University of Mining & TechnologyXuzhouP.R. China
  2. 2.Department of Civil EngineeringUniversity of GlasgowGlasgowUK
  3. 3.School of EngineeringDurham UniversityDurhamUK

Personalised recommendations