Design Issues in a Multiobjective Cellular Genetic Algorithm

  • Antonio J. Nebro
  • Juan J. Durillo
  • Francisco Luna
  • Bernabé Dorronsoro
  • Enrique Alba
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4403)

Abstract

In this paper we study a number of issues related to the design of a cellular genetic algorithm (cGA) for multiobjective optimization. We take as an starting point an algorithm following the canonical cGA model, i.e., each individual interacts with those ones belonging to its neighborhood, so that a new individual is obtained using the typical selection, crossover, and mutation operators within this neighborhood. An external archive is used to store the non-dominated solutions found during the evolution process. With this basic model in mind, there are many different design issues that can be faced. Among them, we focus here on the synchronous/asynchronous feature of the cGA, the feedback of the search experience contained in the archive into the algorithm, and two different replacement strategies. We evaluate the resulting algorithms using a benchmark of problems and compare the best of them against two state-of-the-art genetic algorithms for multiobjective optimization. The obtained results indicate that the cGA model is a promising approach to solve this kind of problem.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. on Evol. Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  2. 2.
    Knowles, J., Corne, D.: The pareto archived evolution strategy: A new baseline algorithm for multiobjective optimization. In: CEC 1999, pp. 9–105 (1999)Google Scholar
  3. 3.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) (2001)Google Scholar
  4. 4.
    Jaeggi, D., Parks, G., Kipouros, T., Clarkson, J.: A multi-objective tabu search algorithm for constrained optimisation problems. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 490–504. Springer, Heidelberg (2005)Google Scholar
  5. 5.
    Nebro, A.J., Luna, F., Alba, E.: New ideas in applying scatter search to multiobjective optimization. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 443–458. Springer, Heidelberg (2005)Google Scholar
  6. 6.
    Alba, E., Tomassini, M.: Parallelism and Evolutionary Algorithms. IEEE Trans. on Evolutionary Computation 6(5), 443–462 (2002)CrossRefGoogle Scholar
  7. 7.
    Cantú-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Publishers, Dordrecht (2000)MATHGoogle Scholar
  8. 8.
    Manderick, B., Spiessens, P.: Fine-grained parallel genetic algorithm. In: Proc. of the Third Int. Conf. on Genetic Algorithms (ICGA), pp. 428–433 (1989)Google Scholar
  9. 9.
    Whitley, D.: Cellular genetic algorithms. In: Forrest, S. (ed.) Proc. of the Fifth International Conference on Genetic Algorithms (ICGA), p. 658. Morgan Kaufmann, San Francisco (1993)Google Scholar
  10. 10.
    Tomassini, M.: Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time. Natural Computing Series. Springer, Heidelberg (2005)MATHGoogle Scholar
  11. 11.
    Alba, E., Dorronsoro, B.: The exploration/exploitation tradeoff in dynamic cellular evolutionary algorithms. IEEE Trans. on Evol. Computation 9(2), 126–142 (2005)CrossRefGoogle Scholar
  12. 12.
    Alba, E., Dorronsoro, B., Giacobini, M., Tomasini, M.: Decentralized Cellular Evolutionary Algorithms. In: Olariu, S., Zomaya, A.Y. (eds.) Handbook of Bioinspired Algorithms and Applications, pp. 103–120. CRC Press, Boca Raton (2006)Google Scholar
  13. 13.
    Laumanns, M., Rudolph, G., Schwefel, H.P.: A Spatial Predator-Prey Approach to Multi-Objective Optimization: A Preliminary Study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) Parallel Problem Solving from Nature - PPSN V. LNCS, vol. 1498, pp. 241–249. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  14. 14.
    Murata, T., Gen, M.: Cellular Genetic Algorithm for Multi-Objective Optimization. In: Proc. of the 4th Asian Fuzzy System Symposium, pp. 538–542 (2002)Google Scholar
  15. 15.
    Kirley, M.: MEA: A metapopulation evolutionary algorithm for multi-objective optimisation problems. In: CEC 2001, pp. 949–956. IEEE Computer Society Press, Los Alamitos (2001)Google Scholar
  16. 16.
    Alba, E., Dorronsoro, B., Luna, F., Nebro, A.J., Bouvry, P., Hogie, L.: A Cellular Multi-Objective Genetic Algorithm for Optimal Broadcasting Strategy in Metropolitan MANETs. Computer Communications (To appear, 2006)Google Scholar
  17. 17.
    Grimme, C., Schmitt, K.: Inside a predator-prey model for multi-objective optimization: A second study. In: Cattolico, M. (ed.) GECCO-2006, Seattle, Washington, USA, July 8–12 2006, pp. 707–714. ACM Press, New York (2006)CrossRefGoogle Scholar
  18. 18.
    Nebro, A.J., Durillo, J.J., Luna, F., Dorronsoro, B., Alba, E.: A cellular genetic algorithm for multiobjective optimization. In: Pelta, D.A., Krasnogor, N. (eds.) NICSO 2006, pp. 25–36 (2006)Google Scholar
  19. 19.
    Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. IEEE Trans. on Evol. Computation 8(2), 173–195 (2000)CrossRefGoogle Scholar
  20. 20.
    Huband, S., Barone, L., While, R.L., Hingston, P.: A scalable multi-objective test problem toolkit. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 280–295. Springer, Heidelberg (2005)Google Scholar
  21. 21.
    Durillo, J.J., Nebro, A.J., Luna, F., Dorronsoro, B., Alba, E.: jMetal: A java framework for developing multiobjective optimization metaheuristics. Technical Report ITI-2006.10, Dpto. de Lenguajes y Ciencias de la Computación (2006)Google Scholar
  22. 22.
    Deb, K., Agrawal, R.B.: Simulated Binary Crossover for Continuous Search Space. Complex Systems 9, 115–148 (1995)MathSciNetMATHGoogle Scholar
  23. 23.
    Demšar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)Google Scholar
  24. 24.
    Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)MATHGoogle Scholar
  25. 25.
    Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective Evolutionary Algorithm Research: A History and Analysis. Technical Report TR-98-03, Dept. Elec. Comput. Eng., Air Force Inst. Technol. (1998)Google Scholar
  26. 26.
    Zitzler, E., Thiele, L.: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Trans. on Evol. Computation 3(4), 257–271 (1999)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Antonio J. Nebro
    • 1
  • Juan J. Durillo
    • 1
  • Francisco Luna
    • 1
  • Bernabé Dorronsoro
    • 1
  • Enrique Alba
    • 1
  1. 1.Departamento de Lenguajes y Ciencias de la Computación, E.T.S. Ingeniería Informática, Campus de Teatinos, 29071 Málaga (Spain) 

Personalised recommendations