Name
Abstract | ||
|---|---|---|
AbstractArtificial bee colony (ABC) is a novel population-based optimization method, having the advantage of less control parameters, being easy to implement, and having strong global optimization ability. However, ABC algorithm has some shortcomings concerning its position-updated equation, which is skilled in global search and bad at local search. In order to coordinate the ability of global and local search, we first propose a self-adaptive ABC algorithm (denoted as SABC) in which an improved position-updated equation is used to guide the search of new candidate individuals. In addition, good-point-set approach is introduced to produce the initial population and scout bees. The proposed SABC is tested on 12 well-known problems. The simulation results demonstrate that the proposed SABC algorithm has better search ability with other several ABC variants. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1155/2017/4851493 | Periodicals |
Field | DocType | Volume |
Population,Artificial bee colony algorithm,Mathematical optimization,Global optimization,Self adaptive,Function optimization,Artificial intelligence,Local search (optimization),Mathematics | Journal | 2017 |
Issue | ISSN | Citations |
1 | 1687-5249 | 0 |
PageRank | References | Authors |
0.34 | 18 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingzhu Tang | 1 | 22 | 1.91 |
| Long Wen | 2 | 1 | 1.71 |
| Huawei Wu | 3 | 0 | 2.37 |
| Kang Zhang | 4 | 1054 | 126.26 |
| Yuri A. W. Shardt | 5 | 37 | 7.10 |