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Particle swarm optimization for the assignment of facilities to locations. (English) Zbl 1070.90005

Onwubolu, Godfrey C. (ed.) et al., New optimization techniques in engineering. Berlin: Springer (ISBN 3-540-20167-X/hbk). Studies in Fuzziness and Soft Computing 141, 567-584 (2004).
Summary: 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 and 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.
For the entire collection see [Zbl 1051.90002].

MSC:

90-06 Proceedings, conferences, collections, etc. pertaining to operations research and mathematical programming
90B80 Discrete location and assignment
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