Cultural Algorithms (CAs) are one of the metaheuristics which can be adapted in order to work in multi-objectives optimization environments. On the other hand, Bi-Objective Uncapacitated Facility Location Problem (BOUFLP) and particularly Uncapacitated Facility Location Problem (UFLP) are well know problems in literature. However, only few articles have applied evolutionary multi-objective (EMO) algorithms to these problems and articles presenting CAs applied to the BOUFLP have not been found. In this article we presents a Bi-Objective Cultural Algorithm (BOCA) which was applied to the Bi-Objective Uncapacitated Facility Location Problem (BOUFLP) and it obtain an important improvement in comparison with other well-know EMO algorithms such as PAES and NSGA-II. The considered criteria were cost minimization and coverage maximization. The different solutions obtained with the CA were compared using an hypervolume S metric. © 2011 Springer-Verlag.
CITATION STYLE
Cabrera, G., Rubio, J. M., Díaz, D., Fernández, B., Cubillos, C., & Soto, R. (2011). A cultural algorithm applied in a bi-objective uncapacitated facility location problem. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6576 LNCS, pp. 477–491). https://doi.org/10.1007/978-3-642-19893-9_33
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