Name
Abstract | ||
|---|---|---|
Although Wolf Pack Algorithm (WPA) is a novel optimal algorithm with good performance, there is still room for improvement with respect to its convergence. In order to speed up its convergence and strengthen the search ability, we improve WPA with the Differential Evolution (DE) elite set strategy. The new proposed algorithm is called the WPADEES for short. WPADEES is faster than WPA in convergence, and it has a more feasible adaptability for various optimizations. Six standard benchmark functions are applied to verify the effects of these improvements. Our experiments show that the performance of WPADEES is superior to the standard WPA and other intelligence optimal algorithms, such as GA, DE, PSO, and ABC, in several situations. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1587/transinf.2017EDL8201 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
Wolf Pack Algorithm (WPA), Differential Evolution (DE), swarm intelligence, evolutionary computation | Computer vision,Elite,Computer science,Differential evolution,Artificial intelligence | Journal |
Volume | Issue | ISSN |
E101D | 7 | 1745-1361 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiayang Chen | 1 | 0 | 1.01 |
| Chaojing Tang | 2 | 29 | 15.21 |
| Jian Wang | 3 | 3 | 1.82 |
| Lei Zhang | 4 | 11 | 3.75 |
| Qingkun Meng | 5 | 0 | 0.34 |