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A Dynamic Search Space Strategy for Swarm Intelligence

  • Shui-Ping Zhang
  • Wang BiEmail author
  • Xue-Jiao Wang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 575)

Abstract

As an appendix which is designed to embed in one of the complete swarm intelligence algorithms, the novel strategy, named dynamic-search-spaces (DS) is proposed to deal with the premature convergence of those algorithms. For realizing the decrement of search space, the differences or the distances between individual sites and the site of the global performance are to form the threshold of the self-adaption system. Once the value reached by calculating the quotient of sum of those sitting near the global performance and others over a stated percentage, the system is working to readjust the borders of search space by the site of the global performance. After each readjustment, the re-initialize to distribute individuals in the whole search space should be achieved to enhance individuals’ vitality which prove away from the premature convergence. Meanwhile, the simpler verifications are provided. The improvements of results are exhibited embedding in the genetic algorithm, the particle swarm optimization and the differential evolution. This dynamic search space scheme can be embedded in most of swarm intelligence algorithms easily abstract environment.

Keywords

Swarm intelligence Self-adaption Search space Particle swarm optimization 

References

  1. 1.
    Zhang, J., Xin, B., Chen, J.: Hybridizing differential evolution and particle swarm optimization to design powerful optimizers: a review and taxonomy. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42(5), 744–767 (2012)CrossRefGoogle Scholar
  2. 2.
    Poli, R.: Analysis of the publications on the applications of particle swarm optimisation. J. Artif. Evol. Appl. 2008, 1–10 (2008)Google Scholar
  3. 3.
    Khare, A., Rangnekar, S.: A review of particle swarm optimization and its applications in solar photovoltaic system. Appl. Soft Comput. 13(5), 2997–3006 (2013)CrossRefGoogle Scholar
  4. 4.
    Ghaemi, R., Sulaiman, N., Ibrahim, H., Mustapha, N.: A review: accuracy optimization in clustering ensembles using genetic algorithms. Artif. Intell. Rev. 35(4), 287–318 (2011)CrossRefGoogle Scholar
  5. 5.
    Huang, J.H., Chen, T.Y.: Application of data mining in a global optimization algorithm. Adv. Eng. Softw. 66(12), 24–33 (2013)Google Scholar
  6. 6.
    Ortiz, E.: Improving the training time of support vector regression algorithms through novel hyper-parameters search space reductions. Neurocomputing 72, 3683–3691 (2009)CrossRefGoogle Scholar
  7. 7.
    Bland, J.A., Nolle, L.: Self-adaptive stepsize search for automatic optimal design. Knowl.-Based Syst. 29(3), 75–82 (2012)Google Scholar
  8. 8.
    Tizhoosh, H.R.: Opposition-based learning: a new scheme for machine intelligence. In: International Conference on Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, pp. 695–701 (2005)Google Scholar
  9. 9.
    Tang, K., Li, X., Suganthan, P.N., Yang, Z., Weise, T.: Benchmark functions for the cec2010 special session and competition on large-scale global optimization. Nature Inspired Computation and Applications Laboratory (2010)Google Scholar
  10. 10.
    Goldberg, D.E., Sastry, K.: Genetic algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman, Boston (1989)zbMATHGoogle Scholar
  11. 11.
    Zhang, W.S., Li, K., Yu, X.: Improved evolutionary algorithm and its application to solving complex optimization problems. Appl. Res. Comput. 29(4), 1223–1226 (2012)Google Scholar
  12. 12.
    Zhang, W.J., Xie, X.F., Bi, D.C.: Handling boundary constraints for numerical optimization by particle swarm flying in periodic search space. In: Congress on Evolutionary Computation, CEC2004, vol. 2. IEEE (2004)Google Scholar
  13. 13.
    Helwig, S., Branke, J., Mostaghim, S.: Experimental analysis of bound handling techniques in particle swarm optimization. IEEE Trans. Evol. Comput. 17(2), 259–271 (2013)CrossRefGoogle Scholar
  14. 14.
    Gandomi, A.H., Yang, X.-S.: Evolutionary boundary constraint handling scheme. Neural Comput. Appl. 21(6), 1449–1462 (2012)CrossRefGoogle Scholar
  15. 15.
    Chu, W., Gao, X., Sorooshian, S.: Handling boundary constraints for particle swarm optimization in high-dimensional search space. Inf. Sci. 181(20), 4569–4581 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.Faculty of Information EngineeringJiangxi University of Science and TechnologyGanzhouChina

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