Improved artificial bee colony algorithm for global optimization

The artificial bee colony algorithm is a relatively new optimization technique. This paper presents an improved artificial bee colony (IABC) algorithm for global optimization. Inspired by differential evolution (DE) and introducing a parameter M. we propose two improved solution search equations, namely ABC/best/1 and ABC/rand/1. Then, in order to take advantage of them and avoid the shortages of them, we use a selective probability p to control the frequency of introducing ABC/rand/1 and ABC/best/1 and get a new search mechanism. In addition, to enhance the global convergence speed, when producing the initial population, both the chaotic systems and the opposition-based learning method are employed. Experiments are conducted on a suite of unimodal/multimodal benchmark functions. The results demonstrate the good performance of the IABC algorithm in solving complex numerical optimization problems when compared with thirteen recent algorithms. (C) 2011 Elsevier B.V. All rights reserved.