Name
Abstract | ||
|---|---|---|
The shortest path problem lies at the heart of network flows that seeks for the paths with minimum cost from source node to sink node in networks. This paper presents a hybrid genetic-ant colony optimization algorithmic approach to the optimal path selection problem. First, some existing solutions for the optimal path selection problem are analyzed, and some shortages and flaws are pointed out. Second, the data organization method for road network based on the graph theory is proposed. Furthermore, the optimal path selection algorithm integrated of sinusoidal probability transfer rules, pheromone update strategy and dual population is presented. Finally, the experimental results show that the proposed algorithm speeds up the convergence rate and improves the efficiency. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1080/10798587.2016.1196926 | INTELLIGENT AUTOMATION AND SOFT COMPUTING |
Keywords | Field | DocType |
The optimal path selection, Genetic algorithm, ACO | Ant colony optimization algorithms,Computer science,Artificial intelligence,Suurballe's algorithm,Shortest Path Faster Algorithm,Widest path problem,K shortest path routing,Mathematical optimization,Shortest path problem,Selection algorithm,Algorithm,Yen's algorithm,Machine learning | Journal |
Volume | Issue | ISSN |
23 | 2 | 1079-8587 |
Citations | PageRank | References |
2 | 0.36 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiping Liu | 1 | 11 | 6.00 |
| Shenghua Xu | 2 | 11 | 5.02 |
| Fuhao Zhang | 3 | 14 | 2.69 |
| Liang Wang | 4 | 1567 | 158.46 |