Abstract
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.
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Change history
11 April 2019
In the original version of the book, the first affiliation “Princeton University, Princeton, NJ 08544, USA” has to be removed for chapter authors “Lu Sun” and “Yuan Tian” in chapters “A Minimum Spanning Tree Algorithm Based on Fuzzy Weighted Distance” and “Cuckoo Search Algorithm Based on Stochastic Gradient Descent”, respectively.
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Acknowledgement
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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Tian, Y., Liang, Yq., Peng, Yj. (2019). Cuckoo Search Algorithm Based on Stochastic Gradient Descent. In: Krömer, P., Zhang, H., Liang, Y., Pan, JS. (eds) Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications. ECC 2018. Advances in Intelligent Systems and Computing, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-03766-6_10
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DOI: https://doi.org/10.1007/978-3-030-03766-6_10
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