Name
Abstract | ||
|---|---|---|
The hybrid artificial bee colony (ABC) algorithm with differential evolution (DE) techniques (HABCwDE) is proposed for numerical optimization in this paper. The HABCwDE adopts multiple candidate solution generation strategies (CSGSes) from DE techniques to generate new solutions in the framework of the ABC algorithm. In the HABCwDE algorithm, three CSGSes and three groups of parameter settings are employed. The performance of HABCwDE and some other evolutionary algorithms are tested on 26 state-of-the-art benchmark functions. Experimental results demonstrate that HABCwDE is very competitive, and that it is an effective way to improve the performance of ABC algorithm by employing CSGSes from DE techniques. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1080/10798587.2014.962239 | INTELLIGENT AUTOMATION AND SOFT COMPUTING |
Keywords | Field | DocType |
Genetic algorithm, Differential evolution, Computational intelligence, Particle swarm optimization, Global optimization, Artificial bee colony | Artificial bee colony algorithm,Mathematical optimization,Evolutionary algorithm,Computer science,Meta-optimization,Evolutionary computation,Differential evolution,Artificial intelligence,Imperialist competitive algorithm,Population-based incremental learning,Genetic algorithm,Machine learning | Journal |
Volume | Issue | ISSN |
21 | 4 | 1079-8587 |
Citations | PageRank | References |
4 | 0.41 | 20 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Xue | 1 | 871 | 60.17 |
| Shuiming Zhong | 2 | 79 | 7.30 |
| Tinghuai Ma | 3 | 314 | 40.76 |
| Jie Cao | 4 | 627 | 73.36 |