Advertisement

Functional-Specialization Multi-Objective Real-Coded Genetic Algorithm: FS-MOGA

  • Naoki Hamada
  • Jun Sakuma
  • Shigenobu Kobayashi
  • Isao Ono
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)

Abstract

This paper presents a Genetic Algorithm (GA) for multi-objective function optimization. In multi-objective function optimization, we believe that GA should adaptively switch search strategies in the early stage and the last stage for effective search. Non-biased sampling and family-wise alternation are suitable to overcome local Pareto optima in the early stage of search, and extrapolation-directed sampling and population-wise alternation are effective to cover the Pareto front in the last stage. These situation-dependent requests make it difficult to keep good performance through the whole search process by repeating a single strategy. We propose a new GA that switches two search strategies, each of which is specialized for global and local search, respectively. This is done by utilizing the ratio of non-dominated solutions in the population. We examine the effectiveness of the proposed method using benchmarks and a real-world problem.

Keywords

Local Search Pareto Front Pareto Optimal Solution Global Search Objective Space 
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.
    Deb, K., Kalyanmoy, D.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Inc., New York (2001)zbMATHGoogle Scholar
  2. 2.
    Huband, S., Hingston, P., Barone, L., While, L.: A review of multiobjective test problems and a scalable test problem toolkit. Evol. Comput. 10(5), 477–506 (2006)CrossRefGoogle Scholar
  3. 3.
    Iorio, A.W., Li, X.: Rotationally invariant crossover operators in evolutionary multi-objective optimization. In: Wang, T.-D., Li, X.-D., Chen, S.-H., Wang, X., Abbass, H.A., Iba, H., Chen, G.-L., Yao, X. (eds.) SEAL 2006. LNCS, vol. 4247, pp. 310–317. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Harada, K., Ikeda, K., Kobayashi, S.: Hybridization of genetic algorithm and local search in multiobjective function optimization: Recommendation of GA then LS. In: GECCO 2006, pp. 667–674. ACM, New York (2006)Google Scholar
  5. 5.
    Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimisation: NSGA-II. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  6. 6.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. In: Evolutionary Methods for Design, Optimisation and Control with Application to Industrial Problems (EUROGEN 2001), International Center for Numerical Methods in Engineering (CIMNE), pp. 95–100 (2002)Google Scholar
  7. 7.
    Kita, H., Ono, I., Kobayashi, S.: Multi-parental extension of the unimodal normal distribution crossover for real-coded genetic algorithms. In: CEC 1999, pp. 1581–1587 (1999)Google Scholar
  8. 8.
    Tsutsui, S., Yamamura, M., Higuchi, T.: Multi-parent recombination with simplex crossover in real coded genetic algorithms. In: GECCO 1999, pp. 657–664 (1999)Google Scholar
  9. 9.
    Ballester, P.J., Carter, J.N.: An effective real-parameter genetic algorithm with parent centric normal crossover for multimodal optimisation. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 901–913. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Hillermeier, C.: Nonlinear Multiobjective Optimization: A Generalized Homotopy Approach. International Series of Numerical Mathematics, vol. 25. Birkhäuser Verlag, Basel (2001)CrossRefGoogle Scholar
  11. 11.
    Knowles, J.D., Corne, D.W.: On metrics for comparing non-dominated sets. In: CEC 2002, pp. 711–716 (2002)Google Scholar
  12. 12.
    Tominaga, D., Koga, N., Okamoto, M.: Efficient numerical optimization algorithm based on genetic algorithm for inverse problem. In: GECCO 2000, pp. 252–258 (2000)Google Scholar
  13. 13.
    Kikuchi, S., Tominaga, D., Arita, M., Takahashi, K., Tomita, M.: Dynamic modeling of genetic networks using genetic algorithm and S-system. Bioinformatics 19(5), 643–650 (2003)CrossRefGoogle Scholar
  14. 14.
    Laumanns, M., Thiele, L., Deb, K., Zitzler, E.: Combining convergence and diversity in evolutionary multiobjective optimization. Evol. Comput. 10(3), 263–282 (2002)CrossRefGoogle Scholar
  15. 15.
    Zitzler, E., Thiele, L., Laumanns, M., Foneseca, C.M., Grunert da Fonseca, V.: Performance assessment of multiobjective optimizers: An analysis and review. Evol. Comput. 7(2), 117–132 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Naoki Hamada
    • 1
  • Jun Sakuma
    • 1
  • Shigenobu Kobayashi
    • 1
  • Isao Ono
    • 1
  1. 1.Interdisciplinary Graduate School of Science and EngineeringTokyo Institute of TechnologyYokohamaJapan

Personalised recommendations