An improved cuckoo search combing artificial bee colony operator with opposition-based learning
Shao-Qiang Ye, Fang-Ling Wang, Yun Ou, Cheng-Xu Zhang, Kai-Qing Zhou · 2021 China Automation Congress (CAC) · 2021
Aiming at the existed drawbacks of the cuckoo search (CS) algorithm, an improved CS combing artificial bee colony operator with opposition-based learning (ICS-ABC- OBL) is proposed to overcome the problems of low convergence efficiency and weak ability to jump out local optimum. The proposed algorithm uses the artificial bee colony (ABC) operator to replace the random migration stage for improving the local exploitation efficiency of the CS algorithm. On the other hand, the opposition-based learning (OBL) operator and dynamic search domain adaptively are introduced, the modified ICS-ABC-OBL can effectively avoid falling into the local optimum by expanding the search solution space and enhancing the balance between exploration and exploitation of the CS algorithm so that the convergence accuracy and efficiency of the algorithm are further improved. Finally, ten classic benchmark functions are used to test the effectiveness of the proposed algorithm. The results show that the ICS-ABC-OBL has strong robustness and adaptability.