Elite Opposition-based Artificial Bee Colony Algorithm for Global Optimization

Numerous problems in engineering and science can be transformed into optimization problems. Artificial bee colony (ABC) algorithm is a newly developed stochastic optimization algorithm and has been successfully used in many areas. However, due to the stochastic characteristics of the 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 the exploitation capacity. In the experiments, EOABC is tested on a set of numerical benchmark test functions, and is compared with some other ABC algorithms. The comparisons indicate that EOABC can obtain competitive results on the majority of the test functions.