¹Dalian University of Technology / Faculty of Infrastructure Engineering, Dalian, China
²Dalian University of Technology / School of Hydraulic Engineering, Dalian, China

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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.

Index Terms—swarm intelligence; artificial bee colony algorithm; pattern search; numerical optimization; critical circular slip surface

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Cite: Fei Kang, Junjie Li, Zhenyue Ma, Haojin Li, "Artificial Bee Colony Algorithm with Local Search for Numerical Optimization," Journal of Software vol. 6, no. 3, pp. 490-497, 2011.