Artificial bee colony algorithm based on knowledge fusion

Artificial bee colony (ABC) algorithm is one of the branches of swarm intelligence. Several studies proved that the original ABC has powerful exploration and weak exploitation capabilities. Therefore, balancing exploration and exploitation is critical for ABC. Incorporating knowledge in intelligent optimization algorithms is important to enhance the optimization capability. In view of this, a novel ABC based on knowledge fusion (KFABC) is proposed. In KFABC, three kinds of knowledge are chosen. For each kind of knowledge, the corresponding utilization method is designed. By sensing the search status, a learning mechanism is proposed to adaptively select appropriate knowledge. Thirty-two benchmark problems are used to validate the optimization capability of KFABC. Results show that KFABC outperforms nine ABC and three differential evolution algorithms.