Name
Abstract | ||
|---|---|---|
The artificial bee colony (ABC) algorithm is a stochastic, population-based evolutionary method that can be applied to a wide range of problems, including global optimization. The paper proposes a variation on the traditional ABC algorithm, called the artificial bee colony programming, or ABCP, employing randomized distribution, bit hyper-mutation and a novel crossover operator to significantly improve the performance of the original algorithm. Application of the new ABC algorithm on fifteen benchmark optimization problems shows a marked improvement in performance over the traditional ABC. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICNC.2009.377 | Natural Computation, 2009. ICNC '09. Fifth International Conference |
Keywords | Field | DocType |
crossover operator,evolutionary computation,population-based evolutionary method,global optimization,clone selection algorithm,randomized distribution,optimization,artificial immune systems,artificial bee colony programming | Artificial bee colony algorithm,Population,Mathematical optimization,Artificial immune system,Algorithm design,Crossover,Global optimization,Computer science,Evolutionary computation,Artificial intelligence,Optimization problem,Machine learning | Conference |
Volume | ISBN | Citations |
4 | 978-0-7695-3736-8 | 3 |
PageRank | References | Authors |
0.43 | 6 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xingbao Liu | 1 | 5 | 1.81 |
| Zixing Cai | 2 | 1525 | 66.96 |