| dc.contributor.author | Abraham, Ajith | |
| dc.contributor.author | Jatoth, Ravi Kumar | |
| dc.contributor.author | Rajasekhar, A. | |
| dc.date.accessioned | 2012-05-11T12:19:13Z | |
| dc.date.available | 2012-05-11T12:19:13Z | |
| dc.date.issued | 2012 | |
| dc.identifier.citation | Journal of Computational and Theoretical Nanoscience. 2012, vol 9, no. 2, p. 249-257. | cs |
| dc.identifier.issn | 1546-1955 | |
| dc.identifier.uri | http://hdl.handle.net/10084/90440 | |
| dc.description.abstract | Artificial Bee Colony Algorithm (ABCA) is a new population-based meta-heuristic approach inspired by the foraging behaviour of bees. This article describes an application of a novel Hybrid Differential Artificial Bee Colony Algorithm (HDABCA), which combines Differential Evolution strategy with Artificial Bee Colony algorithm. We illustrate the proposed method using several test functions and also compared with classical differential evolution algorithm and artificial bee colony algorithm. Simulation results illustrate that the proposed method is very efficient. | cs |
| dc.language.iso | en | cs |
| dc.publisher | Elsevier | cs |
| dc.relation.ispartofseries | Journal of Computational and Theoretical Nanoscience | cs |
| dc.relation.uri | https://doi.org/10.1166/jctn.2012.2019 | cs |
| dc.subject | artificial bee colony | cs |
| dc.subject | differential evolution | cs |
| dc.subject | optimization models | cs |
| dc.title | Hybrid differential artificial bee colony algorithm | cs |
| dc.type | article | cs |
| dc.identifier.location | Není ve fondu ÚK | cs |
| dc.identifier.doi | 10.1166/jctn.2012.2019 | |
| dc.type.status | Peer-reviewed | cs |
| dc.description.source | Web of Science | cs |
| dc.description.volume | 9 | cs |
| dc.description.issue | 2 | cs |
| dc.description.lastpage | 257 | cs |
| dc.description.firstpage | 249 | cs |
| dc.identifier.wos | 000302837800010 | |