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An experimental analysis of design choices of multi-objective ant colony optimization algorithms

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Abstract

There have been several proposals on how to apply the ant colony optimization (ACO) metaheuristic to multi-objective combinatorial optimization problems (MOCOPs). This paper proposes a new formulation of these multi-objective ant colony optimization (MOACO) algorithms. This formulation is based on adding specific algorithm components for tackling multiple objectives to the basic ACO metaheuristic. Examples of these components are how to represent multiple objectives using pheromone and heuristic information, how to select the best solutions for updating the pheromone information, and how to define and use weights to aggregate the different objectives. This formulation reveals more similarities than previously thought in the design choices made in existing MOACO algorithms. The main contribution of this paper is an experimental analysis of how particular design choices affect the quality and the shape of the Pareto front approximations generated by each MOACO algorithm. This study provides general guidelines to understand how MOACO algorithms work, and how to improve their design.

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Acknowledgements

The research leading to the results presented in this paper has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement nº246939. This work was also supported by the Meta-X project, funded by the Scientific Research Directorate of the French Community of Belgium. Manuel López–Ibáñez and Thomas Stützle acknowledge support of the F.R.S.-FNRS of which they are a post-doctoral researcher and a research associate, respectively.

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  1. CoDE–IRIDIA, Université libre de Bruxelles (ULB), CP 194/6, Av. F. Roosevelt 50, 1050, Brussels, Belgium

    Manuel López-Ibáñez & Thomas Stützle

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  1. Manuel López-Ibáñez
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  2. Thomas Stützle
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Correspondence to Manuel López-Ibáñez.

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This is an extended version of a paper that received the best paper award of the Ant Colony Optimization and Swarm Intelligence track of the GECCO 2010 conference.

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López-Ibáñez, M., Stützle, T. An experimental analysis of design choices of multi-objective ant colony optimization algorithms. Swarm Intell 6, 207–232 (2012). https://doi.org/10.1007/s11721-012-0070-7

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