A Continuous Particle Swarm Optimization Algorithm for Uncapacitated Facility Location Problem
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.
KeywordsParticle Swarm Optimization Local Search Position Vector Particle Swarm Optimization Algorithm Local Search Algorithm
Unable to display preview. Download preview PDF.
- 1.Eberhart, R.C., Kennedy, J.: A New Optimizer Using Particle Swarm Theory. In: Proc. of the 6th Int. Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43 (1995)Google Scholar
- 2.Cornuéjols, G., Nemhauser, G.L., Wolsey, L.A.: The Uncapacitated Facility Location Problem. In: Discrete Location Theory, pp. 119–171. Wiley Interscience, New York (1990)Google Scholar
- 14.Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm intelligence. Morgan-Kaufmann, San Francisco (2001)Google Scholar
- 15.Beasley, J.E.: OR-Library (2005), http://people.brunel.ac.uk/~mastjjb/jeb/info.html