Name
Abstract | ||
|---|---|---|
The Wolf Pack Algorithm (WPA) is a new optimal algorithm with good performance in optimation, but there is still room for improvement of its convergence. Aiming at enhancing the ability of the local search and increasing the individual diversity in the population of the Wolf Pack Algorithm, a novel hybrid based on Wolf Pack Algorithm and Differential Evolution (DE) was proposed, which named WPA/DE for short. Six standard benchmark functions are applied to verify the effects of these improvements. It is shown that the performance of WPA/DE is superior to, or at least highly competitive with, the standard WPA and other swarm intelligence optimal algorithm, such as GA, DE, PSO, and ABC in most situations. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ISCID.2017.63 | 2017 10th International Symposium on Computational Intelligence and Design (ISCID) |
Keywords | Field | DocType |
Wolf Pack Algorithm,Differential Evolution,swarm intelligence,evolutionary computation | Convergence (routing),Population,Algorithm design,Computer science,Swarm intelligence,Algorithm,Differential evolution,Local search (optimization),Differential evolution algorithm | Conference |
Volume | ISSN | ISBN |
1 | 2165-1701 | 978-1-5386-3676-3 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiayang Chen | 1 | 0 | 0.34 |
| Chaojing Tang | 2 | 1 | 0.70 |
| Jian Wang | 3 | 3 | 1.82 |
| lei zhang | 4 | 403 | 143.70 |
| Yi Liu | 5 | 1 | 1.38 |