Name
Abstract | ||
|---|---|---|
Ant colony optimization (ACO) can be used to solve complex optimization problems in engineering, economic management and military strategy. Most of these are NP hard problems, which are difficult to solve with traditional methods. An improved parallel ACO algorithm based on pattern learning is proposed in this paper. It extracts parameters automatically to reduce solution space and enhance calculation efficiency. Various parameters in the algorithm are analyzed, and a refining strategy is formed according to ACO’s characteristics. The parallel ACO algorithm is carried out under the MIC/CPU architecture, and it can significantly enhance performance. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ISPA/IUCC.2017.00083 | 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC) |
Keywords | Field | DocType |
Pattern learning,Ant Colony Optimization,MIC | Ant colony optimization algorithms,Pattern learning,Computer science,Parallel processing,Parallel computing,Support vector machine,Cpu architecture,Human–computer interaction,Optimization problem | Conference |
ISSN | ISBN | Citations |
2158-9178 | 978-1-5386-3791-3 | 0 |
PageRank | References | Authors |
0.34 | 8 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaotian Jin | 1 | 0 | 0.34 |
| Wenbo Zheng | 2 | 10 | 6.96 |
| Shaocong Mo | 3 | 0 | 0.34 |
| Yili Qu | 4 | 1 | 1.38 |
| Xin Jin | 5 | 98 | 26.32 |
| Jiangwei Zhou | 6 | 0 | 0.34 |
| Pengfei Duan | 7 | 2 | 1.11 |
| Tao Zheng | 8 | 68 | 15.81 |