Abstract
Several multi-objective ant colony optimization (MOACO) algorithms use a parameter λ to balance the importance of each one of the objectives in the search. In this paper we have studied two different schemes of application for that parameter: keeping it constant, or changing its value during the algorithm running, in order to decide the configuration which yields the best set of solutions. We have done it considering our MOACO algorithm, named hCHAC, and two other algorithms from the literature, which have been adapted to solve the same problem. The experiments show that the use of a variable value for λ yields a wider Pareto set, but keeping a constant value for this parameter let to find better results for any objective.
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Mora, A.M., Merelo, J.J., Castillo, P.A., Laredo, J.L.J., García-Sánchez, P., Arenas, M.G. (2010). Studying the Influence of the Objective Balancing Parameter in the Performance of a Multi-Objective Ant Colony Optimization Algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Studies in Computational Intelligence, vol 284. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12538-6_14
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DOI: https://doi.org/10.1007/978-3-642-12538-6_14
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