A comparative study of Artificial Bee Colony algorithm


KARABOĞA D. , AKAY B.

APPLIED MATHEMATICS AND COMPUTATION, vol.214, no.1, pp.108-132, 2009 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 214 Issue: 1
  • Publication Date: 2009
  • Doi Number: 10.1016/j.amc.2009.03.090
  • Title of Journal : APPLIED MATHEMATICS AND COMPUTATION
  • Page Numbers: pp.108-132
  • Keywords: Swarm intelligence, Evolution strategies, Genetic algorithms, Differential evolution, Particle swarm optimization, Artificial Bee Colony algorithm, Unconstrained optimization, ABC OPTIMIZATION ALGORITHM, EVOLUTION

Abstract

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. (C) 2009 Elsevier Inc. All rights reserved.