Performance of Multiple Objective Evolutionary Algorithms on a Distribution System Design Problem - Computational Experiment

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1993)


The paper presents a comparative experiment with four multiple objective evolutionary algorithms on a real life combinatorial optimization problem. The test problem corresponds to the design of a distribution system. The experiment compares performance of a Pareto ranking based multiple objective genetic algorithm (Pareto GA), multiple objective multiple start local search (MOMSLS), multiple objective genetic local search (MOGLS) and an extension of Pareto GA involving local search (Pareto GLS). The results of the experiment clearly indicate that the methods hybridizing recombination and local search operators by far outperform methods that use one of the operators alone. Furthermore, MOGLS outperforms Pareto GLS.


Local Search Distribution Center Temporary Population Travelling Salesperson Problem Local Search Operator 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ackley D. H. A connectionist machine for genetic hillclimbing. Kluwer Academic Press, Boston, 1987.Google Scholar
  2. 2.
    Galinier P., Hao J.-K. Hybrid evolutionary algorithms for graph coloring. Technical Report, Parc Scientifique Georges Besse, Nimes, 1999.Google Scholar
  3. 3.
    Fonseca C.M., Fleming P.J. (1993), Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In S. Forrest (Ed.), Genetic Algorithms: Proceedings of 5th International Conference, San Mateo, CA, Morgan Kaufmann, 416–423.Google Scholar
  4. 4.
    Goldberg D.E. Genetic algorithms for search, optimization, and machine learning. Reading, MA, Addison-Wesley, 1989.Google Scholar
  5. 5.
    Hansen P.H, Jaszkiewicz A. Evaluating quality of approximations to the non-dominated set. Technical Report, Department of Mathematical Modelling, Technical University of Denmark, IMM-REP-1998-7, 1998.Google Scholar
  6. 6.
    Ishibuchi H. Murata T. Multi-Objective Genetic Local Search Algorithm and Its Application to Flowship Scheduling. IEEE Transactions on Systems, Man and Cybernetics, 28, 3, 392–403, 1998.CrossRefGoogle Scholar
  7. 7.
    Jaszkiewicz A. Genetic local search for multiple objective combinatorial optimization, Technical Report RA-014/98, Institute of Computing Science, Poznan University of Technology, 1998Google Scholar
  8. 8.
    Jaszkiewicz A. On the performance of multiple objective genetic local search on the 0/1 knapsack problem. A comparative experiment. Submitted to IEEE Transactions on Evolutionary Computation.Google Scholar
  9. 9.
    Knowles J.D., Corne D.W. A Comparison of Diverse Approaches to Memetic Multiobjective Combinatorial Optimization, In Proceedings of the 2000 Genetic and Evolutionary Computation Conference Workshop Program, pages 103–108, Las Vegas, Nevada, July 2000.Google Scholar
  10. 10.
    Merz P., Freisleben B., Genetic Local Search for the TSP: New Results, In Proceedings of the 1997 IEEE International Conference on Evolutionary Computation, IEEE Press, pp. 159–164, 1997.Google Scholar
  11. 11.
    Steuer R.E. Multiple Criteria Optimization-Theory, Computation and Application, Wiley, New York, 1986.zbMATHGoogle Scholar
  12. 12.
    Ulungu E.L., Teghem J., Fortemps Ph., Tuyttens D.. MOSA method: a tool for solving multiobjective combinatorial optimization problems. Journal of Multi-Criteria Decision Analysis, 8, 221–236, 1999.zbMATHCrossRefGoogle Scholar
  13. 13.
    Taillard É. D. Comparison of iterative searches for the quadratic assignment problem, Location science, 3, 87–105, 1995.zbMATHGoogle Scholar
  14. 14.
    Van Veldhuizen D.A. (1999). Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations. PhD thesis, Department of Electrical and Computer Engineering. Graduate School of Engineering. Air Force Institute of Technology, Wright-Patterson AFB, Ohio, May 1999.Google Scholar
  15. 15.
    Zitzler E., Thiele L. Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach, IEEE Transactions on Evolutionary Computation, Vol. 3, No. 4, pp. 257–271, November, 1999.CrossRefGoogle Scholar
  16. 16.
  17. 17.

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  1. 1.Institute of Computing SciencePoznań University of Technology ulPoznańpoland

Personalised recommendations