An Empirical Analysis of Multiple Objective Ant Colony Optimization Algorithms for the Bi-criteria TSP

  • Carlos García-Martínez
  • Oscar Cordón
  • Francisco Herrera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3172)


The difficulty to solve multiple objective combinatorial optimization problems with traditional techniques has urged researchers to look for alternative, better performing approaches for them. Recently, several algorithms have been proposed which are based on the Ant Colony Optimization metaheuristic. In this contribution, the existing algorithms of this kind are reviewed and experimentally tested in several instances of the bi-objective traveling salesman problem, comparing their performance with that of two well-known multi-objective genetic algorithms.


Pareto Front Travel Salesman Problem Pheromone Trail Heuristic Information Pheromone Matrix 
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.
    Barán, B., Schaerer, M.: A Multiobjective Ant Colony System for Vehicle Routing Problem with Time Windows. In: Proc. Twenty first IASTED International Conference on Applied Informatics, Insbruck, Austria, February 10-13, pp. 97–102 (2003)Google Scholar
  2. 2.
    Cardoso, P., Jesús, M., Márquez, A.: MONACO - Multi-Objective Network Optimisation Based on an ACO. In: Proc. X Encuentros de Geometría Computacional, Seville, Spain, June 16-17 (2003)Google Scholar
  3. 3.
    Coello, C.A., Van Veldhuizen, D.A., Lamant, G.B.: Evolutionary Algorithms for Solving Multi-objective Problems. Kluwer, Dordrecht (2002)zbMATHGoogle Scholar
  4. 4.
    Doerner, K., Hartl, R.F., Teimann, M.: Are COMPETants More Competent for Problem Solving? The Case of Full Truckload Transportation, Central European Journal of Operations Research (CEJOR) 11(2), 115–141 (2003)zbMATHGoogle Scholar
  5. 5.
    Doerner, K., Gutjahr, W.J., Hartl, R.F., Strauss, C., Stummer, C.: Pareto Ant Colony Optimization: AMetaheuristic Approach toMultiobjective Portfolio Selection, Annals of Operations Research (2004) (to appear)Google Scholar
  6. 6.
    Dorigo, M., Stützle, T.: The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances. In: Glover, F., Kochenberger, G.A. (eds.) Handbook of Metaheuristics, Kluwer, Dordrecht (2003)Google Scholar
  7. 7.
    Gambardella, L., Taillard, E., Agazzi, G.: MACS-VRPTW: A Multiple ACS for Vehicle Routing Problems with Time Windows. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 73–76. McGraw-Hill, New York (1999)Google Scholar
  8. 8.
    Iredi, S., Merkle, D., Middendorf, M.: Bi-Criterion Optimization with Multi Colony Ant Algorithms. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 359–372. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  9. 9.
    Jaszkiewicz, A.: Genetic Local Search for Multi-objective Combinatorial Optimization. European Journal of Operational Research 137(1), 50–71 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Mariano, C.E., Morales, E.: A Multiple Objective Ant-Q Algorithm for the Design of Water Distribution Irrigation Networks, Technical Report HC-9904, Instituto Mexicano de Tecnología del Agua (June 1999)Google Scholar
  11. 11.
    Ulungu, E.L., Teghem, J.: Multi-objective Combinatorial Optimization: A Survey. Journal of Multi-Criteria Decision Analysis 3, 83–104 (1994)zbMATHCrossRefGoogle Scholar
  12. 12.
    Zitzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8(2), 173–195 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Carlos García-Martínez
    • 1
  • Oscar Cordón
    • 1
  • Francisco Herrera
    • 1
  1. 1.Dept. of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain

Personalised recommendations