Name
Abstract | ||
|---|---|---|
Almost all heuristic optimization procedures require the presence of a well-tuned set of parameters. The tuning of these parameters is usually a critical issue and may entail intensive computational requirements. We propose a fast and effective approach composed of two distinct stages. In the first stage, a genetic algorithm is applied to a small subset of representative problems to determine a few robust parameter sets. In the second stage, these sets of parameters are the starting points for a fast local search procedure, able to more deeply investigate the space of parameter sets for each problem to be solved. This method is tested on a parametric version of the Clarke and Wright algorithm and the results are compared with an enumerative parameter-setting approach previously proposed in the literature. The results of our computational testing show that our new parameter-setting procedure produces results of the same quality as the enumerative approach, but requires much shorter computational time. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1057/palgrave.jors.2602488 | Journal of The Operational Research Society |
Keywords | Field | DocType |
genetic algorithm | Mathematical optimization,Vehicle routing problem,Heuristic,Computer science,Algorithm,Combinatorial optimization,Parametric statistics,Heuristics,Parameter space,Local search (optimization),Genetic algorithm | Journal |
Volume | Issue | ISSN |
59 | 11 | 0160-5682 |
Citations | PageRank | References |
12 | 0.84 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maria Battarra | 1 | 185 | 14.70 |
| Bruce L. Golden | 2 | 113 | 23.39 |
| Daniele Vigo | 3 | 2054 | 149.20 |