Elite Opposition-based Artificial Bee Colony Algorithm for Global Optimization

Abstract

Numerous problems in engineering and science can be converted into optimization problems. Artificial bee colony (ABC) algorithm is a newly developed stochastic optimization algorithm and has been widely used in many areas. However, due to the stochastic characteristics of its solution search equation, the traditional ABC algorithm often suffers from poor exploitation. Aiming at this weakness of the traditional ABC algorithm, in this paper, we propose an enhanced ABC algorithm with elite opposition-based learning strategy (EOABC). In the proposed EOABC, it executes the elite opposition-based learning strategy with a preset learning probability to enhance its exploitation capacity. In the numerical experiments, EOABC is tested on a set of numerical benchmark test functions, and is compared with some other ABCs. The comparisons confirm that EOABC can achieve competitive results on the majority of the benchmark test functions.