The impact of design choices of multiobjective antcolony optimization algorithms on performance: an experimental study on the biobjective TSP

  • Authors:

  • Manuel López-Ibáñez;Thomas Stützle

  • Affiliations:

  • Université Libre de Bruxelles, Brussels, Belgium;Université Libre de Bruxelles, Brussels, Belgium

  • Venue:
  • Proceedings of the 12th annual conference on Genetic and evolutionary computation
  • Year:
  • 2010

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Abstract

Over the last few years, there have been a number of proposals of ant colony optimization (ACO) algorithms for tackling multiobjective combinatorial optimization problems. These proposals adapt ACO concepts in various ways, for example, some use multiple pheromone matrices and multiple heuristic matrices and others use multiple ant colonies. In this article, we carefully examine several of the most prominent of these proposals. In particular, we identify commonalities among the approaches by recasting the original formulation of the algorithms in different terms. For example, several proposals described in terms of multiple colonies can be cast equivalently using a single ant colony, where ants use different weights for aggregating the pheromone and/or the heuristic information. We study algorithmic choices for the various proposals and we identify previously undetected trade-offs in their performance