Name
Abstract | ||
|---|---|---|
Optimization methods based on complete neighborhood exploration such as Tabu Search are impractical against large neighborhood problems. Strategies of candidate list propose a solution to reduce the neighborhood exploration complexity. We propose in this paper a generic Tabu Search algorithm using adaptive candidate list strategy based on two alternate candidate lists. Each candidate list strategy corresponds to a given search phase: intensification or diversification. The optimization algorithm uses a Tabu list containing the variables causing loops. The paper proposes a classification of Tabu tenure managing in the literature and presents a new and original Tabu tenure adaptation mechanism. The generic method is tested on the k-coloring problem and compared with some best methods published in the literature. Obtained results show the competitiveness of the method. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-78604-7_1 | EvoCOP |
Keywords | Field | DocType |
candidate list,tabu tenure managing,adaptive tabu tenure computation,candidate list strategy corresponds,adaptive candidate list strategy,generic tabu search algorithm,tabu search,complete neighborhood exploration,alternate candidate list,tabu list,local search,original tabu tenure adaptation | Hill climbing,Data mining,Mathematical optimization,Guided Local Search,Local optimum,Optimization algorithm,Local search (optimization),Tabu search,Mathematics,Computation | Conference |
Volume | ISSN | ISBN |
4972 | 0302-9743 | 3-540-78603-1 |
Citations | PageRank | References |
3 | 0.41 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Isabelle Devarenne | 1 | 8 | 1.53 |
| Hakim Mabed | 2 | 100 | 14.07 |
| Alexandre Caminada | 3 | 107 | 23.61 |