Gbest-guided artificial bee colony algorithm for numerical function optimization

• Published in: Applied mathematics and computation Vol. 217; no. 7; pp. 3166 - 3173
• Main Authors: Zhu, Guopu, Kwong, Sam
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.12.2010; Elsevier
• Subjects: Algorithms; Artificial bee colony algorithm; Biological-inspired optimization algorithm; Calculus of variations and optimal control; Colonies; Differential evolution; Exact sciences and technology; Exploitation; Genetic algorithm; Mathematical analysis; Mathematical models; Mathematics; Measure and integration; Numerical analysis; Numerical analysis. Scientific computation; Numerical function optimization; Numerical methods in mathematical programming, optimization and calculus of variations; Numerical methods in optimization and calculus of variations; Optimization; Particle swarm optimization; Sciences and techniques of general use; Searching; Differential evolution; Artificial bee colony algorithm; Biological-inspired optimization algorithm; Genetic algorithm; Numerical function optimization; Particle swarm optimization; Optimization method; Global solution; Search algorithm; Experimental result; Numerical analysis; Applied mathematics; Variational calculus; Mathematical programming
• ISSN: 0096-3003; 1873-5649
• DOI: 10.1016/j.amc.2010.08.049

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.