Abstract
In this paper, we solve the Set Covering Problem with a meta-optimization approach. One of the most popular models among facility location models is the Set Covering Problem. The meta-level metaheuristic operates on solutions representing the parameters of other metaheuristic. This approach is applied to an Artificial Bee Colony metaheuristic that solves the non-unicost set covering. The Artificial Bee Colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. This metaheuristic owns a parameter set with a great influence on the effectiveness of the search. These parameters are fine-tuned by a Genetic Algorithm, which trains the Artificial Bee Colony metaheuristic by using a portfolio of set covering problems. The experimental results show the effectiveness of our approach which produces very near optimal scores when solving set covering instances from the OR-Library.
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Acknowledgements
Broderick Crawford is supported by grant CONICYT/FONDECYT/REGULAR 1171243 and Ricardo Soto is supported by Grant CONICYT/FONDECYT/REGULAR/1160455, Gino Astorga is supported by Postgraduate Grant, Pontificia Universidad Catolica de Valparaíso, 2015 and José García is supported by INF-PUCV 2016. This research was partially funded by CORFO Program Ingeniería 2030 PUCV - Consortium of Chilean Engineering Faculties.
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Crawford, B., Soto, R., Monfroy, E., Astorga, G., García, J., Cortes, E. (2017). A Meta-Optimization Approach for Covering Problems in Facility Location. In: Figueroa-García, J., López-Santana, E., Villa-Ramírez, J., Ferro-Escobar, R. (eds) Applied Computer Sciences in Engineering. WEA 2017. Communications in Computer and Information Science, vol 742. Springer, Cham. https://doi.org/10.1007/978-3-319-66963-2_50
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DOI: https://doi.org/10.1007/978-3-319-66963-2_50
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