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Distance Based Ranking in Many-Objective Particle Swarm Optimization

  • Sanaz Mostaghim
  • Hartmut Schmeck
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)

Abstract

Optimization problems with many objectives open new issues for multi-objective optimization algorithms and particularly Particle Swarm Optimization. Many of the existing algorithms are able to solve problems of low number of objectives, but as soon as we increase the number of objectives, their performances get even worse than random search methods. This paper gives an overview on Multi-objective Particle Swarm Optimization when having many objectives and parameters. Furthermore, two new variants of MOPSO are proposed which are based on ranking of the non-dominated solutions. The proposed distance based ranking in MOPSO improves the quality of the solutions for even very large objective and parameter spaces. The quality of the new proposed MOPSO methods has been tested and compared to the random search and NSGA-II methods. The tests cover 3 to 20 objectives and 20 to 100 parameters.

Keywords

Particle Swarm Optimization Multiobjective Optimization Ranking Method Tournament Selection Random Search Method 
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.

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References

  1. 1.
    Alvarez-Benitez, J., Everson, R., Fieldsend, J.: A MOPSO algorithm based exclusively on Pareto dominance concepts. In: Evolutionary Multi-Criterion Optimization, pp. 459–473 (2005)Google Scholar
  2. 2.
    Bentley, P., Wakefield, J.: Finding acceptable Pareto-optimal solutions using multiobjective genetic algorithms. In: Soft Computing in Engineering Design and Manufacturing. Springer, Heidelberg (1997)Google Scholar
  3. 3.
    Branke, J., Mostaghim, S.: About selecting the personal best in multi-objective particle swarm optimization. In: Parallel Problem Solving from Nature, pp. 523–532 (2006)Google Scholar
  4. 4.
    Coello Coello, C., Van Veldhuizen, D., Lamont, G.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, Dordrecht (2002)CrossRefzbMATHGoogle Scholar
  5. 5.
    Corne, D., Knowles, J.: Techniques for highly multiobjective optimisation: Some nondominated points are better than others. In: Genetic and Evolutionary Computation Conference (2007)Google Scholar
  6. 6.
    Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley, Chichester (2001)zbMATHGoogle Scholar
  7. 7.
    Deb, K., Agrawal, S., Pratab, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Parallel Problem Solving from Nature, pp. 849–858 (2000)Google Scholar
  8. 8.
    Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable multi-objective optimization test problems. In: Congress on Evolutionary Computation, pp. 825–830 (2002)Google Scholar
  9. 9.
    di Pierro, F., Djordjevic, S., Khu, S., Savic, D., Walters, G.: Automatic calibration of urban drainage model using a novel multi-objective GA. In: Urban Drainage Modelling, pp. 41–52 (2004)Google Scholar
  10. 10.
    Drechsler, D., Drechsler, R., Becker, B.: Multi-objective optimisation based on relation favour. In: Evolutionary Multi-Criterion Optimization, pp. 156–166 (2001)Google Scholar
  11. 11.
    Engelbrecht, A.: Fundamentals of Computational Swarm Intelligence. John Wiley, Chichester (2006)Google Scholar
  12. 12.
    Fieldsend, J., Singh, S.: A multi-objective algorithm based upon particle swarm optimisation, an efficient data structure and turbulence. In: The U.K. Workshop on Computational Intelligence, pp. 34–44 (2002)Google Scholar
  13. 13.
    Kennedy, J., Eberhart, R.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)Google Scholar
  14. 14.
    Knowles, J., Corne, D.: Quantifying the effects of objective space dimension in evolutionary multiobjective optimization. In: Evolutionary Multi-Criterion Optimization, pp. 757–771 (2007)Google Scholar
  15. 15.
    Li, X.: A non-dominated sorting particle swarm optimizer for multiobjective optimization. In: Genetic and Evolutionary Computation, pp. 37–48 (2003)Google Scholar
  16. 16.
    Li, X.: Better spread and convergence: Particle swarm multiobjective optimization using the maximin fitness function. In: Genetic and Evolutionary Computation, pp. 117–128 (2004)Google Scholar
  17. 17.
    Lovberg, M., Krink, T.: Extending particle swarm optimization with self-organized criticality. In: Conference on Evolutionary Computation, pp. 1588–1593 (2002)Google Scholar
  18. 18.
    Mostaghim, S., Teich, J.: Strategies for finding good local guides in multi-objective particle swarm optimization. In: IEEE Swarm Intelligence Symposium (2003)Google Scholar
  19. 19.
    Purshouse, R.: On the Evolutionary Optimisation of Many Objectives. PhD thesis, Department of Automatic Control and Systems Engineering, University of Sheffield, UK (2003)Google Scholar
  20. 20.
    Purshouse, R., Fleming, P.: Evolutionary multi-objective optimisation: An exploratory analysis. In: Congress on Evolutionary Computation, pp. 2066–2073 (2003)Google Scholar
  21. 21.
    Reyes-Sierra, M., Coello Coello, 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
  22. 22.
    Xie, X., Zhang, W., Yang, Z.: Adaptive particle swarm optimization on individual level. In: The Sixth International Conference on Signal Processing, pp. 1215–1218 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Sanaz Mostaghim
    • 1
  • Hartmut Schmeck
    • 1
  1. 1.Institute AIFBUniversity of KarlsruheGermany

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