An artificial bee colony algorithm with elite memory strategy

In recently years, due to its power global search ability, artificial bee colony (ABC) has been successfully applied in many real-world problems. But its shortcoming of slow convergence speed still constraints the further applications. In this paper, for further enhancing its merits and conquering this shortcoming, we propose three improved strategies into ABC algorithm. First, we introduce a new array to preserve some elites of population ever achieved. Based on this, the new updating equations are proposed in our paper. Finally, a new updating mechanism for scout bee is proposed to learn from the defined array for further accelerating the convergence rate of population. Compared with the compared modern evolutionary algorithms, experimental results verify our proposed algorithm achieve better performance especially on the accuracy and stability of solutions.