Gbest-guided artificial bee colony algorithm for numerical function optimization
Related Research Unit(s)
|Journal / Publication||Applied Mathematics and Computation|
|Publication status||Published - 1 Dec 2010|
|Link to Scopus||https://www.scopus.com/record/display.uri?eid=2-s2.0-78049297395&origin=recordpage|
Artificial bee colony (ABC) algorithm invented recently by Karaboga is a biological-inspired optimization algorithm, which has been shown to be competitive with some conventional biological-inspired algorithms, such as genetic algorithm (GA), differential evolution (DE) and particle swarm optimization (PSO). However, there is still an insufficiency in ABC algorithm regarding its solution search equation, which is good at exploration but poor at exploitation. Inspired by PSO, we propose an improved ABC algorithm called gbest-guided ABC (GABC) algorithm by incorporating the information of global best (gbest) solution into the solution search equation to improve the exploitation. The experimental results tested on a set of numerical benchmark functions show that GABC algorithm can outperform ABC algorithm in most of the experiments. © 2010 Elsevier Inc. All rights reserved.
- Artificial bee colony algorithm, Biological-inspired optimization algorithm, Differential evolution, Genetic algorithm, Numerical function optimization, Particle swarm optimization
Applied Mathematics and Computation, Vol. 217, No. 7, 01.12.2010, p. 3166-3173.
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Zhu, G & Kwong, S 2010, 'Gbest-guided artificial bee colony algorithm for numerical function optimization', Applied Mathematics and Computation, vol. 217, no. 7, pp. 3166-3173. https://doi.org/10.1016/j.amc.2010.08.049