Name
Abstract | ||
|---|---|---|
Ant Colony optimization takes inspiration from the behavior of real ant colony to solve optimization problems. We attach some constraints to ant colony model and present a parallel constrained ant colony model to solve the image registration problem. The problem is represented by a directed graph so that the objective of the original problem becomes to find the shortest closed circuit on the graph under the problem-specific constraints. A number of artificial ants are distributed on the graph and communicate with one another through the pheromone trails which are a form of the long-term memory guiding the future exploration of the graph. The algorithm supports the parallel computation and facilitates quick convergence to the optimal solution. The performance of the proposed method as compared to those of the genetic-based approaches is very promising. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11816102_1 | ICIC |
Keywords | Field | DocType |
facilitates quick convergence,real ant colony,ant colony algorithm,ant colony model,parallel computation,optimization problem,original problem,ant colony optimization,artificial ant,image registration problem,colony model,long term memory,image registration,parallel computer,ant colony,directed graph | Ant colony optimization algorithms,Convergence (routing),Mathematical optimization,Parallel metaheuristic,Computer science,Meta-optimization,Directed graph,Artificial intelligence,Ant colony,Optimization problem,Machine learning,Metaheuristic | Conference |
Volume | ISSN | ISBN |
4115 | 0302-9743 | 3-540-37277-6 |
Citations | PageRank | References |
2 | 0.37 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wen Peng | 1 | 9 | 1.95 |
| Ruofeng Tong | 2 | 466 | 49.69 |
| Guiping Qian | 3 | 3 | 1.07 |
| Jinxiang Dong | 4 | 311 | 65.36 |