Abstract
iMOACO\(\mathbb {_R}\) is an ant colony optimization algorithm designed to tackle multi-objective optimization problems in continuous search spaces. It is built on top of ACO\(\mathbb {_R}\) and uses the R2 indicator (to improve its performance on high-dimensional objective function spaces) to rank the pheromone archive of the best previously-explored solutions. Due to the utilization of an R2-based selection mechanism, there are typically a large number of tied-ranks in iMOACO\(\mathbb {_R}\)’s pheromone archive. It is worth noting that the solutions of a specific layer share the same importance based on the R2 indicator. A critical issue due to the large number of tied-ranks is a reduction of the algorithm’s exploitation ability. In consequence, in this paper, we propose replacing iMOACO\(\mathbb {_R}\)’s probabilistic solution selection mechanism with a mechanism tailored to these layer-sets. Our proposed layer-set selection uses rank-proportionate (roulette wheel) selection to select a layer, with all the solutions in the layer sharing equally in the layer’s probability. Our experimental evaluation indicates that our proposal, which we call iMOACO\(\mathbb {^{\prime }_{R}}\), performs better than iMOACO\(\mathbb {_R}\) to a statistically significant extent on a large number of benchmark problems having from 3 to 10 objective functions.
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The last author acknowledges support from CONACyT project no. 1920.
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Abdelbar, A.M., Humphries, T., Falcón-Cardona, J.G., Coello Coello, C.A. (2022). An Extension of the iMOACO\(\mathbb {_R}\) Algorithm Based on Layer-Set Selection. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2022. Lecture Notes in Computer Science, vol 13491. Springer, Cham. https://doi.org/10.1007/978-3-031-20176-9_22
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