Journal of Global Optimization

, Volume 39, Issue 3, pp 459–471

A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm

Original Paper

DOI: 10.1007/s10898-007-9149-x

Cite this article as:
Karaboga, D. & Basturk, B. J Glob Optim (2007) 39: 459. doi:10.1007/s10898-007-9149-x

Abstract

Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees’ swarming around their hive is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm. In this work, ABC algorithm is used for optimizing multivariable functions and the results produced by ABC, Genetic Algorithm (GA), Particle Swarm Algorithm (PSO) and Particle Swarm Inspired Evolutionary Algorithm (PS-EA) have been compared. The results showed that ABC outperforms the other algorithms.

Keywords

Swarm intelligenceArtificial bee colonyParticle swarm optimizationGenetic algorithmParticle swarm inspired evolutionary algorithmNumerical function optimization

Copyright information

© Springer Science+Business Media, Inc. 2007

Authors and Affiliations

  1. 1.Department of Computer EngineeringErciyes UniversityKayseriTurkey