Parallel algorithms for continuous competitive location problems

Authors:
Juana L. Redondo;Jose Fernandez;Inmaculada Garcia;Pilar M. Ortigosa
Affiliations:
Department of Computer Architecture and Electronics, University of Almeria, Almeria, Spain;Department of Statistics and Operations Research, University of Murcia, Murcia, Spain;Department of Computer Architecture and Electronics, University of Almeria, Almeria, Spain;Department of Computer Architecture and Electronics, University of Almeria, Almeria, Spain
Venue:
Optimization Methods & Software - THE JOINT EUROPT-OMS CONFERENCE ON OPTIMIZATION, 4-7 JULY, 2007, PRAGUE, CZECH REPUBLIC, PART I
Year:
2008

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Abstract

A continuous location problem in which a firm wants to set up a single new facility in a competitive environment is considered. Other facilities offering the same product or service already exist in the area. Both the location and the quality of the new facility are to be found so as to maximize the profit obtained by the firm. This is a hard-to-solve global optimization problem. An evolutionary algorithm called Universal Evolutionary Global Optimizer (UEGO) seems to be the best procedure to cope with it, but the algorithm needs several hours of CPU time for solving large instances. In this paper, four parallelizations of UEGO are presented. They all are coarse-grain methods which differ in their migratory policies. A computational study is carried out to compare the performance of the parallel algorithms. The results show that one of the parallelizations always gives the best objective function value and has an almost linear speed-up for up to 16 processing elements for large instances.