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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© 2006 Springer-Verlag Berlin Heidelberg
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Sevkli, M., Guner, A.R. (2006). A Continuous Particle Swarm Optimization Algorithm for Uncapacitated Facility Location Problem. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2006. Lecture Notes in Computer Science, vol 4150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11839088_28
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DOI: https://doi.org/10.1007/11839088_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38482-3
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