By Manuel López-Ibáñez and Thomas Stützle
|Table of Contents|
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 algorithm component s
specific for tackling multiple objectives to the basic ACO
metaheuristic. Examples of these components are how to represent
multiple objectives with 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.
Keywords: Ant colony optimization , multi-objective optimization , multi-objective traveling salesman problem , experimental analysis
Download a PDF with supplementary results.
Download the source code of the MOACO framework.