A Learnable Artificial Bee Colony Algorithm for Numerical Function Optimization

To improve optimizing performance of artificial bee colony (ABC), a new algorithm called learnable artificial bee colony (LABC) is presented in this paper. The new algorithm employs some available knowledge from the two optimization phases to guide the next optimization process. Eight benchmark functions are used to validate its optimization effect. The experimental results show that LABC outperforms ABC and particle swarm optimization (PSO) on most benchmark functions. LABC provides a new reference for improving optimization performance of ABC.