Name
Abstract | ||
|---|---|---|
In this paper, we propose an artificial bee group colony algorithm for numerical function optimization, based on the thoughts of group competition and similar property characteristic. Our algorithm contains three optimization strategies including grouping strategy, similar property strategy and competition strategy, which could not only ensure the algorithm finds better solutions stably, but also induce the algorithm to maintain solution diversification. Moreover, the similar property strategy could produce efficient exploring to find better solutions with skipping optimization. We evaluated the performance of our proposed algorithm on some standard numerical benchmark functions. The results demonstrate that our algorithm is able to yield higher quality solutions with faster convergence than either the original ABC or some other authoritative swarm intelligent algorithms. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/SMC.2015.507 | 2015 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2015): BIG DATA ANALYTICS FOR HUMAN-CENTRIC SYSTEMS |
Keywords | Field | DocType |
Artificial bee colony, grouping strategy, group competition, similar property strategy, numerical function optimization | Convergence (routing),Artificial bee colony algorithm,Mathematical optimization,Swarm behaviour,Computer science,Meta-optimization,Numerical function optimization,Algorithm,Evolutionary computation,Multi-swarm optimization,Metaheuristic | Conference |
ISSN | Citations | PageRank |
1062-922X | 0 | 0.34 |
References | Authors | |
9 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gang Yang | 1 | 32 | 9.38 |
| Jieping Xu | 2 | 2 | 0.70 |
| Junyan Yi | 3 | 0 | 1.01 |
| He Zheng | 4 | 0 | 0.68 |
| Zheng Yuan | 5 | 0 | 0.68 |
| Xiaowei Liu | 6 | 0 | 0.68 |