Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
GAS, a concept on modeling species in genetic algorithms
Artificial Intelligence
Computers and Operations Research
Economics of location:: a selective survey
Computers and Operations Research - location science
The ant colony optimization meta-heuristic
New ideas in optimization
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Parallel Processing of Discrete Optimization Problems: DIMACS Workshop April 28-29, 1994
Parallel Processing of Discrete Optimization Problems: DIMACS Workshop April 28-29, 1994
Parallel Computing in Optimization
Parallel Computing in Optimization
Tabu Search
Parallel Processing of Discrete Problems
Parallel Processing of Discrete Problems
Advances in Randomized Parallel Computing
Advances in Randomized Parallel Computing
Using Interval Analysis for Solving Planar Single-Facility Location Problems: New Discarding Tests
Journal of Global Optimization
Reliability and Performance of UEGO, a Clustering-based Global Optimizer
Journal of Global Optimization
On the use of genetic algorithms to solve location problems
Computers and Operations Research - Location analysis
SCOOP: Solving Combinatorial Optimization Problems in Parallel
Solving Combinatorial Optimization Problems in Parallel - Methods and Techniques
Parallel algorithms for continuous competitive location problems
Optimization Methods & Software - THE JOINT EUROPT-OMS CONFERENCE ON OPTIMIZATION, 4-7 JULY, 2007, PRAGUE, CZECH REPUBLIC, PART I
Computers and Operations Research
Solving the multiple competitive facilities location and design problem on the plane
Evolutionary Computation
Heuristics for the facility location and design (1|1)-centroid problem on the plane
Computational Optimization and Applications
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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We consider a continuous location problem in which a firm wants to set up two or more new facilities in a competitive environment. Both the locations and the qualities of the new facilities are to be found so as to maximize the profit obtained by the firm. This hard-to-solve global optimization problem has been addressed in Redondo et al. (Evol. Comput.17(1), 21---53, 2009) using several heuristic approaches. Through a comprehensive computational study, it was shown that the evolutionary algorithm uego is the heuristic which provides the best solutions. In this work, uego is parallelized in order to reduce the computational time of the sequential version, while preserving its capability at finding the optimal solutions. The parallelization follows a coarse-grain model, where each processing element executes the uego algorithm independently of the others during most of the time. Nevertheless, some genetic information can migrate from a processor to another occasionally, according to a migratory policy. Two migration processes, named Ring-Opt and Ring-Fusion2, have been adapted to cope the multiple facilities location problem, and a superlinear speedup has been obtained.