An Improved Artificial Bee Colony Algorithm

Abstract

Artificial bee colony (ABC) algorithm is one of the most recently proposed swarm intelligence algorithms for global 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 7 benchmark functions including a wide range of dimensions. Numerical results show that the new algorithm is promising in terms of convergence speed, success rate and solution accuracy.