Cuckoo Search Algorithm Based on Stochastic Gradient Descent
Cuckoo Search (CS) is a global search algorithm for solving multi-objective optimization problems. Cuckoo Search algorithm is easy to implement and has a few number of control parameters, excellent search path and strong optimization capability. It has been successfully applied to practical problems, such as engineering optimization. To improve the refining ability and convergence rate of CS algorithm, solve the problem of slow convergence rate and unstable search accuracy in later stage, this paper proposes a Cuckoo Search Algorithm based on Stochastic Gradient Descent (SGDCS). This algorithm uses Stochastic Gradient Descent to enhance the search of the local optimum, convergence process and algorithm adaptability, which improves the calculation accuracy and convergence rate of cuckoo search algorithm. The simulation experiments show that the proposed algorithm is simple and efficient, efficiently improves the performances on calculation accuracy and convergence rate on the basis of maintaining the advantages of the standard CS algorithm.
KeywordsCuckoo Search Algorithm Lévy flight Function optimization Stochastic Gradient Descent
We would like to thank the anonymous reviewers for their valuable comments and suggestions. This work is supported by The State Key Research Development Program of China under Grant 2016YFC0801403, Shandong Provincial Natural Science Foundation of China under Grant ZR2018MF009 and ZR2015FM013, the Special Funds of Taishan Scholars Construction Project, and Leading Talent Project of Shandong University of Science and Technology.
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