Gbest-guided artificial bee colony algorithm for numerical function optimization

Guopu ZHU, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

1192 Citations (Scopus)

Abstract

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. © 2010 Elsevier Inc. All rights reserved.
Original languageEnglish
Pages (from-to)3166-3173
JournalApplied Mathematics and Computation
Volume217
Issue number7
DOIs
Publication statusPublished - 1 Dec 2010
Externally publishedYes

Bibliographical note

The authors thank the anonymous reviewers for their valuable comments and suggestions. This work is partly supported by Hong Kong RGC General Research Fund (GRF) 9041495 (CityU 115109) . This work was also supported in part by the NSFC (Grant No. 61003297, 40701050 ).

Keywords

  • Artificial bee colony algorithm
  • Biological-inspired optimization algorithm
  • Differential evolution
  • Genetic algorithm
  • Numerical function optimization
  • Particle swarm optimization

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