Name
Abstract | ||
|---|---|---|
Ant colony system is a well known metaheuristic framework, and many efficient algorithms for different combinatorial optimization problems have been derived from this general framework. In this paper some directions for improving the original framework when a strong local search routine is available, are identified. In particular, some modifications able to speed up the method and make it competitive on large problem instances, on which the original framework tends to be weaker, are described. The resulting framework, called Enhanced Ant Colony System is tested on three well-known combinatorial optimization problems arising in the transportation field. Many new best known solutions are retrieved for the benchmarks available for these optimization problems. (C) 2012 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.ejor.2012.02.038 | European Journal of Operational Research |
Keywords | Field | DocType |
Combinatorial optimization,Metaheuristics,Ant colony optimization,Sequential ordering problems,Team orienteering problems,Probabilistic traveling salesman problems | Ant colony optimization algorithms,Mathematical optimization,Parallel metaheuristic,Extremal optimization,Combinatorial optimization,Local search (optimization),Ant colony,Optimization problem,Mathematics,Metaheuristic | Journal |
Volume | Issue | ISSN |
220 | 3 | 0377-2217 |
Citations | PageRank | References |
32 | 1.06 | 41 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luca Maria Gambardella | 1 | 7926 | 726.40 |
| Roberto Montemanni | 2 | 643 | 44.25 |
| Dennis Weyland | 3 | 108 | 8.43 |