Advertisement

Efficient Multi-Objective Optimization Using 2-Population Cooperative Coevolution

  • Alexandru-Ciprian Zăvoianu
  • Edwin Lughofer
  • Wolfgang Amrhein
  • Erich Peter Klement
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8111)

Abstract

We propose a 2-population cooperative coevolutionary optimization method that can efficiently solve multi-objective optimization problems as it successfully combines positive traits from classic multi-objective evolutionary algorithms and from newer optimization approaches that explore the concept of differential evolution. A key part of the algorithm lies in the proposed dual fitness sharing mechanism that is able to smoothly transfer information between the two coevolved populations without negatively impacting the independent evolutionary process behavior that characterizes each population.

Keywords

continuous multi-objective optimization evolutionary algorithms cooperative coevolution differential evolution 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Coello, C., Lamont, G., Van Veldhuisen, D.: Evolutionary Algorithms for Solving Multi-Objective Problems. Genetic and Evolutionary Computation Series. Springer (2007)Google Scholar
  2. 2.
    Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Systems 9, 115–148 (1995)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  4. 4.
    Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable multi-objective optimization test problems. In: IEEE Congress on Evolutionary Computation (CEC 2002), pp. 825–830. IEEE Press (2002)Google Scholar
  5. 5.
    Deb, K., Goyal, M.: A combined genetic adaptive search (geneas) for engineering design. Computer Science and Informatics 26, 30–45 (1996)Google Scholar
  6. 6.
    Fleischer, M.: The measure of Pareto optima. applications to multi-objective metaheuristics. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 519–533. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Huband, S., Hingston, P., Barone, L., While, L.: A review of multiobjective test problems and a scalable test problem toolkit. IEEE Transactions on Evolutionary Computation 10(5), 477–506 (2006)CrossRefGoogle Scholar
  8. 8.
    Kukkonen, S., Lampinen, J.: GDE3: The third evolution step of generalized differential evolution. In: IEEE Congress on Evolutionary Computation (CEC 2005), pp. 443–450. IEEE Press (2005)Google Scholar
  9. 9.
    Kukkonen, S., Lampinen, J.: Performance assessment of Generalized Differential Evolution 3 with a given set of constrained multi-objective test problems. In: IEEE Congress on Evolutionary Computation (CEC 2009), pp. 1943–1950. IEEE Press (2009)Google Scholar
  10. 10.
    Kursawe, F.: A variant of evolution strategies for vector optimization. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 193–197. Springer, Heidelberg (1991)CrossRefGoogle Scholar
  11. 11.
    Li, H., Zhang, Q.: Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Computation 13(2), 284–302 (2009)CrossRefGoogle Scholar
  12. 12.
    Price, K., Storn, R., Lampinen, J.: Differential evolution. Springer (1997)Google Scholar
  13. 13.
    Robič, T., Filipič, B.: DEMO: Differential evolution for multiobjective optimization. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 520–533. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  14. 14.
    Zaharie, D.: A comparative analysis of crossover variants in differential evolution. In: Proceedings of the International Multiconference on Computer Science and Information Technology, IMCSIT 2007, pp. 171–181. PTI, Wisla (2007)Google Scholar
  15. 15.
    Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8(2), 173–195 (2000)CrossRefGoogle Scholar
  16. 16.
    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), pp. 95–100. International Center for Numerical Methods in Engineering (CIMNE) (2002)Google Scholar
  17. 17.
    Zăvoianu, A.-C., Bramerdorfer, G., Lughofer, E., Silber, S., Amrhein, W., Klement, E.P.: A hybrid soft computing approach for optimizing design parameters of electrical drives. In: Snasel, V., Abraham, A., Corchado, E.S. (eds.) SOCO Models in Industrial & Environmental Appl. AISC, vol. 188, pp. 347–358. Springer, Heidelberg (2013)Google Scholar
  18. 18.
    Zăvoianu, A.C., Lughofer, E., Koppelstätter, W., Weidenholzer, G., Amrhein, W., Klement, E.P.: On the performance of master-slave parallelization methods for multi-objective evolutionary algorithms. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part II. LNCS, vol. 7895, pp. 122–134. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alexandru-Ciprian Zăvoianu
    • 1
    • 3
  • Edwin Lughofer
    • 1
  • Wolfgang Amrhein
    • 2
    • 3
  • Erich Peter Klement
    • 1
    • 3
  1. 1.Department of Knowledge-based Mathematical Systems/Fuzzy Logic LaboratoryJohannes Kepler University of LinzLinz-HagenbergAustria
  2. 2.Institute for Electrical Drives and Power ElectronicsJohannes Kepler University of LinzAustria
  3. 3.ACCM, Austrian Center of Competence in MechatronicsLinzAustria

Personalised recommendations