The Importance of Scalability When Comparing Dynamic Weighted Aggregation and Pareto Front Techniques

  • Grzegorz Drzadzewski
  • Mark Wineberg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3871)


The performance of the Dynamic Weight Aggregation system as applied to a Genetic Algorithm (DWAGA) and NSGA-II are evaluated and compared against each other. The algorithms are run on 11 two-objective test functions, and 2 three-objective test functions to observe the scalability of the two systems. It is discovered that, while the NSGA-II performs better on most of the two-objective test functions, the DWAGA can outperform the NSGA-II on the three-objective problems. We hypothesize that the DWAGA’s archive helps keep the searching population size down since it does not have to both search and store the Pareto front simultaneously, thus improving both the computation time and the quality of the front.


Pareto Front Multiobjective Optimization Objective Problem Evolutionary Multiobjective Optimization Pareto Archive Evolution Strategy 
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.
    Coello Coello, C.A.: A Short Tutorial on Evolutionary Multiobjective Optimization. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 21–40. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  2. 2.
    Jin, Y., Okabe, T., Sendhoff, B.: Adapting Weighted Aggregation for Multiobjective Evolution Strategies. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 96–110. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  3. 3.
    Jin, Y., Okabe, T., Sendhoff, B.: Dynamic Weighted Aggregation for Evolutionary Multi-Ojbective Optimization: Why Does It Work and How. In: Spector, L., et al. (eds.) GECCO 2001 - Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1042–1049. Morgan Kaufmann, San Francisco (2001)Google Scholar
  4. 4.
    Srinivas, N., Deb, K.: Multi-Objective function optimization using non-dominated sorting genetic algorithms. Evolutionary Computation 2(3), 221–248 (1995)CrossRefGoogle Scholar
  5. 5.
    Deb, K., Goel, T.: Controlled Elitist Non-dominated Sorting Genetic Algorithms. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 67–81. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  6. 6.
    Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolution algorithms: empirical results. Evolutionary Computation 8(2), 173–195 (2000)CrossRefGoogle Scholar
  7. 7.
    Knowles, J.D., Corne, D.W.: Approximating the nondominated front using the Pareto archived evolution strategies. Evolutionary Computation 8(2), 149–172 (2000)CrossRefGoogle Scholar
  8. 8.
    Ang, K.H., Chong, G., Li, Y.: Preliminary Statement on the Current Progress of Multi-Objective Evolutionary Algorithm Performance Measurement. In: Eberhart, R., Fogel, D.B. (eds.) Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), pp. 1139–1144. IEEE Press, Los Alamitos (2002)Google Scholar
  9. 9.
    Jin, Y., Okabe, T., Sendhoff, B.: Solving Three-objective Optimization Problems Using Evolutionary Dynamic Weighted Aggregation: Results and Analysis. In: Proceedings of Genetic and Evolutionary Computation Conference, Chicago, p. 636 (2003)Google Scholar
  10. 10.
    Czyzak, P., Jaszkiewicz, A.: Pareto simulated annealing - a metaheuristic technique for multiple-objective combinatorial optimization. Journal of Multi-Criteria Decision Analysis 7, 34–47 (1998)CrossRefMATHGoogle Scholar
  11. 11.
    Hansen, P.H.: Tabu Search for Multiobjective Optimization: MOTS. In: Proceedings of the 13th International Conference on Multiple Criteria Decision Making (1997)Google Scholar
  12. 12.
    Serafini, P.: Simulated annealing for multi objective optimization problems. In: Tzeng, G.H. (ed.) Multiple Criteria Decision Making: Expand and Enrich the Domains of Thinking and Application, Springer, Heidelberg (1993)Google Scholar
  13. 13.
    Ulungu, E., Teghem, J., Fortemps, P., Tuyytens, D.: MOSA Method: A Tool for Solving Multiobjective Combinatorial Optimization Problems. Journal of Multi-Criteria Decision Analysis 8/4, 221–236 (1999)CrossRefMATHGoogle Scholar
  14. 14.
    Knowles, J., Corne, D.: Memetic Algorithms for Multiobjective Optimization: Issues, Methods and Prospects (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Grzegorz Drzadzewski
    • 1
  • Mark Wineberg
    • 1
  1. 1.Computing and Information ScienceUniversity of GuelphGuelph, OntarioCanada

Personalised recommendations