A detailed analysis of the population-based ant colony optimization algorithm for the TSP and the QAP

  • Authors:

  • Sabrina M. Oliveira;Mohamed Saifullah Hussin;Thomas Stuetzle;Andrea Roli;Marco Dorigo

  • Affiliations:

  • Universite Libre de Bruxelles, Brussels, Belgium;Universite Libre de Bruxelles, Brussels, Belgium;Universite Libre de Bruxelles, Brussels, Belgium;Universita di Bologna, Cesena, Italy;Universite Libre de Bruxelles, Brussels, Belgium

  • Venue:
  • Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
  • Year:
  • 2011

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Abstract

The population-based ant colony optimization algorithm (P-ACO) uses a very different pheromone update when compared to other ACO algorithms. In this work, we study P-ACO's behavior for solving the traveling salesman problem (TSP) and the quadratic assignment problem (QAP). In particular, we investigate the impact of a local search on P-ACO parameters and performance. The results clearly show that P-ACO is a very competitive tool whose parameters and behavior depend strongly on the problem tackled and on whether a local search is used.