An artificial bee colony algorithm with an improved updating strategy
Changwu Ge, Hao Gao · 2021
The artificial bee colony (ABC) algorithm shows a relatively powerful exploration search capability but show convergence rate, especially on unimodal functions. In this paper, an improved artificial bee colony algorithm is introduced to shorten its computation time. In the proposed algorithm, two novel update equations, utilizing the social experience of the whole population, are proposed to boost the performance of employed bees and onlooker bees respectively. The effectiveness of our algorithm is validated through the basic benchmark functions. Furthermore, a model of feed-forward artificial neural network is also employed to verify the effectiveness of our algorithm. The experimental results show the IUABC algorithm achieves better performance than the other compared algorithms.