A comparative study of Artificial Bee Colony algorithm

• Published in: Applied mathematics and computation Vol. 214; no. 1; pp. 108 - 132
• Main Authors: Karaboga, Dervis, Akay, Bahriye
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.08.2009; Elsevier
• Subjects: Artificial Bee Colony algorithm; Calculus of variations and optimal control; Differential evolution; Evolution strategies; Exact sciences and technology; Genetic algorithms; Mathematical analysis; Mathematics; Measure and integration; Numerical analysis; Numerical analysis. Scientific computation; Numerical methods in mathematical programming, optimization and calculus of variations; Particle swarm optimization; Sciences and techniques of general use; Swarm intelligence; Unconstrained optimization; Artificial Bee Colony algorithm; Unconstrained optimization; Evolution strategies; Differential evolution; Swarm intelligence; Particle swarm optimization; Genetic algorithms; Optimization method; Numerical analysis; Genetic algorithm; Applied mathematics; Comparative study; Mathematical programming
• ISSN: 0096-3003; 1873-5649
• DOI: 10.1016/j.amc.2009.03.090

Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms. ABC simulates the intelligent foraging behaviour of a honeybee swarm. In this work, ABC is used for optimizing a large set of numerical test functions and the results produced by ABC algorithm are compared with the results obtained by genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm and evolution strategies. Results show that the performance of the ABC is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters.