Advertisement

Preference-Based Multi-Objective Particle Swarm Optimization Using Desirabilities

  • Sanaz Mostaghim
  • Heike Trautmann
  • Olaf Mersmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6239)

Abstract

The integration of experts’ preferences is an important aspect in multi-objective optimization. Usually, one out of a set of Pareto optimal solutions has to be chosen based on expert knowledge. A combination of multi-objective particle swarm optimization (MOPSO) with the desirability concept is introduced to efficiently focus on desired and relevant regions of the true Pareto front of the optimization problem which facilitates the solution selection process. Desirability functions of the objectives are optimized, and the desirability index is used for selecting the global best particle in each iteration. The resulting MOPSO variant DF-MOPSO in most cases exclusively generates solutions in the desired area of the Pareto front. Approximations of the whole Pareto front result in cases of misspecified desired regions.

Keywords

Particle swarm optimization MOPSO desirability function desirability index preferences 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Branke, J., Mostaghim, S.: About selecting the personal best in multi-objective particle swarm optimization. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 523–532. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Branke, J.: Consideration of partial user preferences in evolutionary multiobjective optimization. In: Multiobjective Optimization, pp. 157–178 (2008)Google Scholar
  3. 3.
    Coello Coello, C.A.: Handling preferences in evolutionary multiobjective optimization: A survey. In: Congress on Evolutionary Computation (CEC), pp. 30–37 (2000)Google Scholar
  4. 4.
    Deb, K., Pratap, A., Agarwal, S.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. on Evolutionary Computation 6(8) (2002)Google Scholar
  5. 5.
    Durillo, J.J., Garca-Nieto, J., Nebro, A.J., Coello, C.A.C., Luna, F., Alba, E.: Multi-objective particle swarm optimizers: An experimental comparison. In: Ehrgott, M., Fonseca, C.M., Gandibleux, X., Hao, J.-K., Sevaux, M. (eds.) EMO 2009. LNCS, vol. 5467, pp. 495–509. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Fonseca, C.M., Fleming, J.: An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation 3(1), 1–16 (1995)CrossRefGoogle Scholar
  7. 7.
    Harrington, J.: The desirability function. Industrial Quality Control 21(10), 494–498 (1965)Google Scholar
  8. 8.
    Hettenhausen, J., Lewis, A., Mostaghim, S.: Interactive multi-objective particle swarm optimisation with heatmap visualisation based user interface. Journal of Engineering Optimization 42(2), 119–139 (2010)CrossRefGoogle Scholar
  9. 9.
    Huang, V.L., Qin, A., Deb, K., Suganthan, P., Liang, J., Preuss, M., Huband, S.: Problem definitions for performance assessment on multi-objective optimization algorithms. Technical report, Nanyang Technological University, Singapore (2007)Google Scholar
  10. 10.
    Alvarez-Benitez, J.E., Everson, R.M., Fieldsend, J.E.: A MOPSO algorithm based exclusively on pareto dominance concepts. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 459–473. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Jaszkiewicz, A., Branke, J.: Interactive multiobjective evolutionary algorithms. In: Multiobjective Optimization, pp. 179–193 (2008)Google Scholar
  12. 12.
    Lovberg, M., Krink, T.: Extending particle swarm optimization with self-organized criticality. In: Proceedings of IEEE Conference on Evolutionary Computation, pp. 1588–1593 (2002)Google Scholar
  13. 13.
    Messac, A.: Physical Programming: Effective Optimization for Computational Design. AIAA Journal 34(1), 149–158 (1996)zbMATHCrossRefGoogle Scholar
  14. 14.
    Mostaghim, S., Teich, J.: The role of e-dominance in multi-objective particle swarm optimization methods. In: Proceedings of the Congress on Evolutionary Computation, CEC (2003)Google Scholar
  15. 15.
    Mostaghim, S., Teich, J.: Strategies for finding good local guides in multi-objective particle swarm optimization. In: Swarm Intelligence Symposium, pp. 26–33 (2003)Google Scholar
  16. 16.
    Mostaghim, S., Teich, J.: Covering pareto-optimal fronts by subswarms in multi-objective particle swarm optimization. In: the Proceedings of The Congress on Evolutionary Computation, CEC (2004)Google Scholar
  17. 17.
    Rachmawati, L., Srinivasan, D.: Preference incorporation in multi-objective evolutionary algorithms: A survey. In: Congress on Evolutionary Computation (CEC), pp. 962–968 (2006)Google Scholar
  18. 18.
    Reyes-Sierra, M., Coello, C.C.: Multi-objective particle swarm optimizers: A survey of the state-of-the art. International Journal of Computational Intelligence Research 2(3), 287–308 (2006)MathSciNetGoogle Scholar
  19. 19.
    Sierra, M.R., Coello, C.A.C.: Improving pso-based multi-objective optimization using crowding, mutation and e-dominance. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 505–519. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  20. 20.
    Toscano-Pulido, G., Coello, C.A.C., Santana-Quintero, L.V.: Emopso: A multi-objective particle swarm optimizer with emphasis on efficiency. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 272–285. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  21. 21.
    Trautmann, H., Mehnen, J.: Preference-Based Pareto-Optimization in Certain and Noisy Environments. Engineering Optimization 41, 23–38 (2009)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Trautmann, H., Weihs, C.: On the distribution of the desirability index using Harrington’s desirability function. Metrika 63(2), 207–213 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  23. 23.
    Utyuzhnikov, S., Fantini, P., Guenov, M.: Numerical method for generating the entire Pareto frontier in multiobjective optimization. In: Schilling, R., Haase, W., Periaux, J., Baier, H., Bugeda, G. (eds.) Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems, EUROGEN (2005)Google Scholar
  24. 24.
    Wickramasinghe, U., Li, X.: Integrating user preferences with particle swarms for multi-objective optimization. In: Proceedings of the Conference on Genetic and Evolutionary Computation (GECCO), pp. 745–752 (2008)Google Scholar
  25. 25.
    Xie, X.F., Zhang, W.J., Yang, Z.L.: Adaptive particle swarm optimization on individual level. In: Proceedings of the Sixth International Conference on Signal Processing, pp. 1215–1218 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Sanaz Mostaghim
    • 1
  • Heike Trautmann
    • 2
  • Olaf Mersmann
    • 2
  1. 1.Karlsruhe Institute of TechnologyKarlsruheGermany
  2. 2.TU Dortmund UniversityDortmundGermany

Personalised recommendations