Name
Abstract | ||
|---|---|---|
Frontier search is a best-first graph search technique that allows significant memory savings over previous best-first algorithms.
The fundamental idea is to remove from memory already explored nodes, keeping only open nodes in the search frontier. However,
once the goal node is reached, additional techniques are needed to recover the solution path. This paper describes and analyzes
a path recovery procedure for frontier search applied to multiobjective shortest path problems. Differences with the scalar
case are outlined, and performance is evaluated over a random problem set. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/s10845-008-0169-2 | J. Intelligent Manufacturing |
Keywords | Field | DocType |
Artificial intelligence,Problem solving,Best-first search,Multiobjective state-space search,Frontier search,Shortest path problems | Canadian traveller problem,Mathematical optimization,Shortest path problem,Beam search,Constrained Shortest Path First,Artificial intelligence,Bidirectional search,Longest path problem,Machine learning,Widest path problem,Mathematics,K shortest path routing | Journal |
Volume | Issue | ISSN |
21 | 1 | 0956-5515 |
Citations | PageRank | References |
6 | 0.50 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| L. Mandow | 1 | 86 | 6.91 |
| Jesús De La Cruz | 2 | 271 | 26.56 |