Abstract:In order to enhance the exploitation ability of the basic artificial bee colony (ABC)

a rosenbrock ABC with elite region learning (ERABC) was proposed.The proposed ERABC utilized an enhanced search strategy with elite region learning to maintain the population diversity.Moreover

the rosenbrock’s rotational direction method was employed to improve the exploitation ability.The proposed ERABC was tested on 20 benchmark functions including unimodal

multimodal

and shifted functions.The effects of the improved strategy in ERABC were experimentally investigated.Furthermore

ERABC was compared with some state of the art ABC variants and several related evolutionary algorithms.The experimental results indicated that ERABC enhances the convergence speed and exploitation ability.〖