A comparative study of Artificial Bee Colony algorithm

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 co...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Applied mathematics and computation Jg. 214; H. 1; S. 108 - 132
Hauptverfasser: Karaboga, Dervis, Akay, Bahriye
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier Inc 01.08.2009
Elsevier
Schlagworte:
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
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2009.03.090