Name
Abstract | ||
|---|---|---|
The Bees Algorithm models the foraging behaviour of honey bees in order to solve optimisation problems. The algorithm performs a kind of exploitative neighbourhood search combined with random explorative search. This paper describes the Bees Algorithm, and compares its functioning and performance with those of other state-of-the-art nature-inspired intelligent optimisation methods. Two application cases are presented: the minimisation of a set of well-known benchmark functions, and the training of neural networks to reproduce the inverse kinematics of a robot manipulator. In both cases, the Bees Algorithm proved its effectiveness and speed. Compared with other state-of-the-art methods, the performance of the Bees Algorithm was very competitive. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-642-54455-2_2 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
intelligent optimisation,swarm intelligence,bees algorithm,honey bees | Honey Bees,Inverse kinematics,Swarm intelligence,Minimisation (psychology),Artificial intelligence,Bees algorithm,Engineering,Artificial neural network,Robot manipulator,Machine learning,Foraging | Journal |
Volume | ISSN | Citations |
8342 | 0302-9743 | 2 |
PageRank | References | Authors |
0.39 | 33 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Duc Truong Pham | 1 | 230 | 33.47 |
| Marco Castellani | 2 | 123 | 14.94 |
| Le Thi Hoai An | 3 | 1038 | 80.20 |