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A Cultural Algorithm Applied in a Bi-Objective Uncapacitated Facility Location Problem

  • Guillermo Cabrera
  • José Miguel Rubio
  • Daniela Díaz
  • Boris Fernández
  • Claudio Cubillos
  • Ricardo Soto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6576)

Abstract

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.

Keywords

Bi-Objective Cultural Algorithm Bi-Objective Uncapacitated Facility Location Problem Evolutionary Multi-Objective Optimization S metric 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Guillermo Cabrera
    • 1
  • José Miguel Rubio
    • 1
  • Daniela Díaz
    • 1
  • Boris Fernández
    • 1
  • Claudio Cubillos
    • 1
  • Ricardo Soto
    • 1
  1. 1.Escuela de Ingeniería InformáticaPontificia Universidad Católica de ValparaísoValparaísoChile

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