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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)

Abstract

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.

Keywords

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.

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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

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