Cuckoo Search Algorithm with Balanced Learning to Solve Logistics Distribution Problem

Cuckoo search (CS) has been successfully applied to solve various optimization problems. Despite its simplicity and efficiency, the CS is easy to suffer from the premature convergence and fall into local optimum. In this study, a differential CS extension with balanced learning namely DFCS is proposed. In DFCS, two sets including the better fitness set (FSL) and the better diversity set (DSL) are produced in the iterative process. Two excellent individuals are selected from two sets to participate, which improved the search ability by learning their beneficial behaviors in search process. The performance of DFCS algorithm is evaluated on the logistics distribution problem. The results show that DFCS algorithm has stronger competitiveness in solving logistics distribution problem than CS algorithm.