Details

Autor(en) / Beteiligte

• Zhu, Guopu
• Kwong, Sam

Titel

Gbest-guided artificial bee colony algorithm for numerical function optimization

Ist Teil von

• Applied mathematics and computation, 2010-12, Vol.217 (7), p.3166-3173

Ort / Verlag

Amsterdam: Elsevier Inc

Erscheinungsjahr

2010

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.