Abstract
This chapter describes a new heuristic approach, for minimizing discrete space functions. The new heuristic, particle swarm optimization is applied to the quadratic assignment problem. It is observed from experimentation that the particle swarm optimization approach delivers competitive solutions when compared to ant system, ant system with non-deterministic hill climbing, simulated annealing, tabu search, genetic algorithm, evolutionary strategy, and sampling & clustering for the quadratic assignment problem. By comparing results from the particle swarm optimization and the results of these other best-known heuristics, it will be demonstrated that the particle swarm optimization method converges as much as best-known heuristics for the QAP. The new method requires few control variables, is versatile, is robust, easy to implement and easy to use.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Angeline PJ (1998) Using selection to improve particle swarm optimization, IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, May, 4–9.
Battiti R, Tecchiolli G (1994) The reactive tabu search. ORSA Journal on Computing, 6 (2), 126–140.
Burkard RE, Rendl F (1984) A thermodynamically motivated simulation procedure for combinatorial optimization problems, European Journal of Operational Research, 17, 169–174.
Carlisle A, Dozier G (1998) Adapting Particle Swarm Optimization to Dynamics Environ ments,“ presented at International Conference on Artificial Intelligence, Monte Carlo Resort, Las Vegas, Nevada, USA.
Clerc M (1999) The swarm and the queen: towards a deterministic and adaptive particle swarm optimization, Congress on Evolutionary Computation, Washington D. C., IEEE Service Center, Piscataway, NJ, 1951–1957.
Clerc M, Kennedy J (2002) The particle swarm: explosion, stability, and convergence in a multidimensional complex space, IEEE Transactions on Evolutionary Computation, 6, 58–73.
Connolly DT (1990) An improved annealing scheme for the QAP, European Journal of Operational Research, 46, 93–100.
Cung V-D, Mautor T, Michelon P, Tavares A (1997) A scatter search based approach for the quadratic assignment problem. In Baeck, T., Michalewicz, Z., and Yao, X., editors, Proceedings of ICEC97,165–170, IEEE Press.
Dickey JW, Hopkins JW (1972) Campus building arrangement using TOPAZ. Transportation Science, 6, 59–68.
Dorigo M, Maniezzo V, Colorni A (1996) The Ant System: optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man, and Cybernetics, 26 (1), 29–41.
Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory, Proceedings, Sixth International Symposium on Micro Machine and human science, Nagoya, Japan, IEEE Service Center, Piscataway, NJ, 39–43.
Eberhart RC, Shi Y (1998) Evolving artificial neural networks, presented at International Conference on Neural Networks and Brain, Beijing, P.R.C., PL5–PL13.
Eberhart RC, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization, presented at International Congress on Evolutionary Computation, San Diego, California, 84–88.
Eiselt HA, Laport G (1991), A combinatorial optimization problem arising in dartboard design, Journal of the Operational Research Society, 42, 113–118.
Elshafei AN (1977) Hospital layout as a quadratic assignment problem, Operations Research Quarterly, 28, 167–179.
Fleurent C Ferland JA (1994) Genetic hybrids for the quadratic assignment problem. In Pardalos, P. M., and Wolkowicz, H., editors, Quadratic assignment and related problems, DIMACS Series on Discrete Mathematics and Theoretical Computer Science, 16, American Mathematical Society 173–187.
Geoffrion AM, Graves GW (1976) Scheduling parallel production lines with changeover costs: practical applications of a quadratic assignment/LP approach, Operations Research, 24, 595–610.
He Z, Wei, C (1999) A new population-based incremental learning method for the traveling salesman problem, Congress on Evolutionary Computation, Washington D.C., IEEE.
Johnson DS, McGeoch LA (1997) The travelling salesman problem: a case study in local optimization. In Aarts, E. H. L., and Lenstra, J.K., editors, Local Search in Combinatorial Optimization, John Wiley and Sons 215–310.
Kennedy J (1997) The particle swarm: social adaptation of knowledge, IEEE International Conference on Evolutionary Computation, Indianapolis, Indiana, IEEE Service Center, Piscataway, NJ.
Kennedy J (1999) Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance, Congress on Evolutionary Computation, Washington D.C, IEEE.
Kennedy J (2000) Stereotyping: Improving Particle Swarm Performance With Cluster Analysis,“ presented at Congress on Evolutionary Computation.
Kennedy J, Eberhart RC (1995) Particle swarm optimization, IEEE Proceedings of the International Conference on Neural Networks IV (Perth, Australia), IEEE Service Center, Piscataway, NJ, 1942–1948.
Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm, International Conference on Systems, Man, and Cybernetics.
Kennedy J (1999) Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance, Proceedings of the Congress on Evolutionary Computation, Washington D.C, IEEE Service Center, Piscataway, NJ, 1931–1938.
Krarup J, Pruzan PM (1978) Computer-aided layout design, Mathematical Programming Study, 9, 75–94.
Laporte G, Mercure H (1988) Balancing hydraulic turbine runners: a quadratic assignment problem, European Journal of Operational Research, 35, 378–381.
Li Y, Pardalos PM, Resende MGC (1994) A greedy randomized adaptive search procedure for the quadratic assignment problem. In Pardalos, P. M., and Wolkowicz, H., editors, Quadratic assignment and related problems, DIMACS Series on Discrete Mathematics and Theoretical Computer Science, 16, 237–261, American Mathematical Society, 1994.
Merz P, Freisleben B (1997) A genetic local search approach to the quadratic assignment problem. In BÄack, T., editor, Proceedings of the Seventh International Conference on Genetic Algorithms (ICGA’97), Morgan Kaufmann 465–472.
Nissen V (1994) Solving the Quadratic assignment problem with clues from nature, IEEE Transactions on Neural Networks, 5(1), 66–72.
Nugent CE, Vollman TE, Ruml J (1968) An experimental comparison of techniques for the assignment of facilities to locations, Operations Research, 16, 150–173.
Sahni S, Gonzalez T (1976) P-complete approximation problems, Journal of the ACM, 23, 555–565.
Shi Y, Eberhart RC (1998) A modified particle swarm optimizer, IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, May 4–9.
Skorin-Kapov J (1990) Tabu Search applied to the quadratic assignment problem, ORSA Journal on Computing, 2, 33–45.
Steinberg L (1961) The backboard wiring problem: a placement algorithm, SIAM Review, 3, 37–50.
Taillard ED (1991) Robust taboo search for the quadratic assignment problem, Parallel Computing, 17, 443–455.
Taillard ED (1995) Comparison of iterative searches for the quadratic assignment problem, Location Science, 3, 87–105.
Tate DM, Smith AE (1995) A genetic approach to the quadratic assignment problem, Computers and Operations Research, 22 (1), 73–83.
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Onwubolu, G.C., Sharma, A. (2004). Particle Swarm Optimization for the Assignment of Facilities to Locations. In: New Optimization Techniques in Engineering. Studies in Fuzziness and Soft Computing, vol 141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39930-8_23
Download citation
DOI: https://doi.org/10.1007/978-3-540-39930-8_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05767-0
Online ISBN: 978-3-540-39930-8
eBook Packages: Springer Book Archive