doi: 10.4304/jsw.6.3.490-497
Artificial Bee Colony Algorithm with Local Search for Numerical Optimization
- 1 Dalian University of Technology / Faculty of Infrastructure Engineering, Dalian, China
- 2 Dalian University of Technology / School of Hydraulic Engineering, Dalian, China
Abstract
Artificial bee colony (ABC) algorithm is one of the most recently proposed swarm intelligence algorithms for global numerical optimization. It performs well in most cases; however, there still exist some problems it cannot solve very well. This paper presents a novel hybrid Hooke Jeeves ABC (HJABC) algorithm with intensification search based on the Hooke Jeeves pattern search and the ABC. The main purpose is to demonstrate how the standard ABC can be improved by incorporating a hybridization strategy. The proposed algorithm is tested on a comprehensive set of 36 complex benchmark functions and a slope stability analysis problem including a wide range of dimensions. Comparisons are made with the basic ABC and some recent algorithms. Numerical results show that the new algorithm is promising in terms of convergence speed, success rate and solution accuracy.
Keywords
- swarm
- intelligence;
- artificial
- bee
- colon
How to Cite
Fei Kang, Junjie Li, Zhenyue Ma, and Haojin Li, "Artificial Bee Colony Algorithm with Local Search for Numerical Optimization," Journal of Software, vol. 6, no. 3, pp. 490-497, 2011. https://doi.org/ 10.4304/jsw.6.3.490-497
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).