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A general framework for improving cuckoo search algorithms with resource allocation and re-initialization

Lookup NU author(s): Dr Jie ZhangORCiD


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© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.Cuckoo search (CS) has currently become one of the most favorable meta-heuristic algorithms (MHAs). In this article, a simple yet effective framework is proposed for CS algorithms to reinforce their performance, which contains two core mechanisms: computational resource allocation (CRA) and Gaussian sampling based re-initialization (GSR). The CRA is responsible for allocating more computational resources to promising individuals, thus promoting search efficiency and speeding up convergence, whilst the GSR is introduced to help the algorithm in maintaining population diversity. For testifying the effectiveness and generality of this framework (referred to as AR framework), it is embedded into nine well-established CS algorithms and extensive experiments are conducted on CEC 2013, CEC 2014, and CEC 2017 test suites. Experimental results indicate that the AR framework could bring a significant improvement on the performance of the classical CS as well as its variants, achieving an average efficient rate of 78.97%, 72.59%, and 86.21% on the three test suites, respectively. Besides, the comparisons between the classical CS, its AR framework version, and several other classical MHAs validate the effectiveness of the AR framework again. Additionally, the benefit of each mechanism (i.e., CRA and GSR) and their combination is also ascertained.

Publication metadata

Author(s): Yang Q, Chen Y, Zhang J, Wang Y

Publication type: Article

Publication status: Published

Journal: International Journal of Machine Learning and Cybernetics

Year: 2024

Pages: epub ahead of print

Online publication date: 06/02/2024

Acceptance date: 22/12/2023

ISSN (print): 1868-8071

ISSN (electronic): 1868-808X

Publisher: Springer Science and Business Media Deutschland GmbH


DOI: 10.1007/s13042-023-02081-4


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