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The Scientific World Journal
Volume 2014, Article ID 745921, 10 pages
http://dx.doi.org/10.1155/2014/745921
Research Article

Comparing Evolutionary Strategies on a Biobjective Cultural Algorithm

1Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, 2362807 Valparaíso, Chile
2Universidad Finis Terrae, 7500000 Santiago, Chile
3CIMFAV Facultad de Ingeniería, Universidad de Valparaíso, 2362735 Valparaíso, Chile
4Universidad Autónoma de Chile, 7500138 Santiago, Chile
5Departamento de Computación e Informática, Universidad de Playa Ancha, 33449 Valparaíso, Chile
6Escuela de Ingeniería Industrial, Universidad Diego Portales, 8370109 Santiago, Chile

Received 9 April 2014; Accepted 27 June 2014; Published 31 August 2014

Academic Editor: Xin-She Yang

Copyright © 2014 Carolina Lagos et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Evolutionary algorithms have been widely used to solve large and complex optimisation problems. Cultural algorithms (CAs) are evolutionary algorithms that have been used to solve both single and, to a less extent, multiobjective optimisation problems. In order to solve these optimisation problems, CAs make use of different strategies such as normative knowledge, historical knowledge, circumstantial knowledge, and among others. In this paper we present a comparison among CAs that make use of different evolutionary strategies; the first one implements a historical knowledge, the second one considers a circumstantial knowledge, and the third one implements a normative knowledge. These CAs are applied on a biobjective uncapacitated facility location problem (BOUFLP), the biobjective version of the well-known uncapacitated facility location problem. To the best of our knowledge, only few articles have applied evolutionary multiobjective algorithms on the BOUFLP and none of those has focused on the impact of the evolutionary strategy on the algorithm performance. Our biobjective cultural algorithm, called BOCA, obtains important improvements when compared to other well-known evolutionary biobjective optimisation algorithms such as PAES and NSGA-II. The conflicting objective functions considered in this study are cost minimisation and coverage maximisation. Solutions obtained by each algorithm are compared using a hypervolume S metric.