Advertisement

Incremental Particle Swarm-Guided Local Search for Continuous Optimization

  • Marco A. Montes de Oca
  • Ken Van den Enden
  • Thomas Stützle
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5296)

Abstract

We present an algorithm that is inspired by theoretical and empirical results in social learning and swarm intelligence research. The algorithm is based on a framework that we call incremental social learning. In practical terms, the algorithm is a hybrid between a local search procedure and a particle swarm optimization algorithm with growing population size. The local search procedure provides rapid convergence to good solutions while the particle swarm algorithm enables a comprehensive exploration of the search space. We provide experimental evidence that shows that the algorithm can find good solutions very rapidly without compromising its global search capabilities.

Keywords

Particle Swarm Optimization Local Search Particle Swarm Social Learning Multiagent System 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  2. 2.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. Bradford Books. MIT Press, Cambridge (2004)zbMATHGoogle Scholar
  3. 3.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, USA, pp. 1942–1948. IEEE Press, Los Alamitos (1995)CrossRefGoogle Scholar
  4. 4.
    Montes de Oca, M.A., Stützle, T.: Towards incremental social learning in optimization and multiagent systems. In: Workshop on Evolutionary Computation and Multiagent Systems Simulation of the Genetic and Evolutionary Computation Conference (GECCO 2008), pp. 1939–1944. ACM Press, New York (2008)Google Scholar
  5. 5.
    Powell, M.J.D.: An efficient method for finding the minimum of a function of several variables without calculating derivatives. The Computer Journal 7(2), 155–162 (1964)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Nehaniv, C.L., Dautenhahn, K. (eds.): Imitation and Social Learning in Robots, Humans and Animals: Behavioral, Social and Communicative Dimensions. Cambridge University Press, Cambridge (2007)Google Scholar
  7. 7.
    Curran, D., O’Riordan, C.: Increasing population diversity through cultural learning. Adaptive Behavior 14(4), 315–338 (2006)CrossRefGoogle Scholar
  8. 8.
    Aoki, K., Wakano, Y., Feldman, M.W.: The emergence of social learning in a temporally changing environment: A theoretical model. Current Anthropology 46(2), 334–340 (2005)CrossRefGoogle Scholar
  9. 9.
    Laland, K.N.: Social learning strategies. Learning & Behavior 32(1), 4–14 (2004)CrossRefGoogle Scholar
  10. 10.
    Galef Jr., B.G., Laland, K.N.: Social learning in animals: Empirical studies and theoretical models. BioScience 55(6), 489–499 (2005)CrossRefGoogle Scholar
  11. 11.
    Cavalli-Sforza, L.L., Feldman, M.W.: Cultural Transmission and Evolution. A Quantitative Approach. Princeton University Press, Princeton (1981)zbMATHGoogle Scholar
  12. 12.
    Giraldeau, L.A., Valone, T.J., Templeton, J.J.: Potential disadvantages of using socially acquired information. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 357, 1559–1566 (2002)CrossRefGoogle Scholar
  13. 13.
    Clerc, M., Kennedy, J.: The particle swarm–explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002)CrossRefGoogle Scholar
  14. 14.
    Brent, R.P.: Algorithms for Minimization Without Derivatives. Prentice-Hall, Englewood Cliffs (1973)zbMATHGoogle Scholar
  15. 15.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C. The Art of Scientific Computing, 2nd edn. Cambridge University Press, Cambridge (1992)zbMATHGoogle Scholar
  16. 16.
    Montes de Oca, M.A., Van den Enden, K., Stützle, T.: Incremental particle swarm-guided local search for continuous optimization: Complete results, (2008), http://iridia.ulb.ac.be/supp/IridiaSupp2008-013/
  17. 17.
    Lobo, F.G., Lima, C.F.: Adaptive Population Sizing Schemes in Genetic Algorithms. In: Parameter Setting in Evolutionary Algorithms. 2007 of Studies in Computational Intelligence, vol. 54, pp. 185–204. Springer, Berlin (2007)CrossRefGoogle Scholar
  18. 18.
    Arabas, J., Michalewicz, Z., Mulawka, J.J.: GAVaPS – A genetic algorithm with varying population size. In: Proceedings of the IEEE Conference on Evolutionary Computation, Piscataway, NJ, USA, pp. 73–78. IEEE Press, Los Alamitos (1994)Google Scholar
  19. 19.
    Harik, G.R., Lobo, F.G.: A parameter-less genetic algorithm. In: Banzhaf, W., et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 1999), San Francisco, CA, USA, pp. 258–265. Morgan Kaufmann, San Francisco (1999)Google Scholar
  20. 20.
    Bäck, T., Eiben, A.E., van der Vaart, N.A.L.: An empirical study on GAs “without parameters“. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 315–324. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  21. 21.
    Eiben, A.E., Marchiori, E., Valkó, V.A.: Evolutionary algorithms with on-the-fly population size adjustment. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 41–50. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  22. 22.
    Fernandes, C., Rosa, A.: Self-regulated population size in evolutionary algorithms. In: Parallel Problem Solving from Nature, PPSN IX, Berlin, Germany, pp. 920–929. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  23. 23.
    Coelho, A.L.V., de Oliveira, D.G.: Dynamically tuning the population size in particle swarm optimization. In: Proceedings of the ACM Symposium on Applied Computing (SAC 2008), pp. 1782–1787. ACM Press, New York (2008)CrossRefGoogle Scholar
  24. 24.
    Auger, A., Hansen, N.: A restart CMA evolution strategy with increasing population size. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2005), Piscataway, NJ, USA, pp. 1769–1776. IEEE Press, Los Alamitos (2005)CrossRefGoogle Scholar
  25. 25.
    Chen, J., Qin, Z., Liu, Y., Lu, J.: Particle swarm optimization with local search. In: Proceedings of the International Conference on Neural Networks and Brain (ICNN&B 2005), Piscataway, NJ, USA, pp. 481–484. IEEE Press, Los Alamitos (2005)CrossRefGoogle Scholar
  26. 26.
    Gimmler, J., Stützle, T., Exner, T.E.: Hybrid particle swarm optimization: An examination of the influence of iterative improvement algorithms on performance. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds.) ANTS 2006. LNCS, vol. 4150, pp. 436–443. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  27. 27.
    Das, S., Koduru, P., Gui, M., Cochran, M., Wareing, A., Welch, S.M., Babin, B.R.: Adding local search to particle swarm optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2005), Piscataway, NJ, USA, pp. 428–433. IEEE Press, Los Alamitos (2005)Google Scholar
  28. 28.
    Petalas, Y.G., Parsopoulos, K.E., Vrahatis, M.N.: Memetic particle swarm optimization. Annals of Operations Research 156(1), 99–127 (2007)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Marco A. Montes de Oca
    • 1
  • Ken Van den Enden
    • 2
  • Thomas Stützle
    • 1
  1. 1.IRIDIA, CoDEUniversité Libre de BruxellesBrusselsBelgium
  2. 2.Vrije Universiteit BrusselBrusselsBelgium

Personalised recommendations