Gbest-guided artificial bee colony algorithm for numerical function optimization

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 optimizati...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Applied mathematics and computation Jg. 217; H. 7; S. 3166 - 3173
Hauptverfasser: Zhu, Guopu, Kwong, Sam
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier Inc 01.12.2010
Elsevier
Schlagworte:
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
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2010.08.049