Abstract
In this paper, a continuous Particle Swarm Optimization (PSO) algorithm is presented for the Uncapacitated Facility Location (UFL) problem. In order to improve the solution quality a local search is embedded to the PSO algorithm. It is applied to several benchmark suites collected from OR-library. The results are presented and compared to the results of two recent metaheuristic approaches, namely Genetic Algorithm(GA) and Evolutionary Simulated Annealing (ESA). It is concluded that the PSO algorithm is better than the compared methods and generates more robust results.
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