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            Sine Cosine Algorithm for Optimization

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            Auteur
            Bansal, Jagdish Chand
            Bajpai, Prathu
            Rawat, Anjali
            Nagar, Atulya K.
            Language
            English
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            Résumé
            This open access book serves as a compact source of information on sine cosine algorithm (SCA) and a foundation for developing and advancing SCA and its applications. SCA is an easy, user-friendly, and strong candidate in the field of metaheuristics algorithms. Despite being a relatively new metaheuristic algorithm, it has achieved widespread acceptance among researchers due to its easy implementation and robust optimization capabilities. Its effectiveness and advantages have been demonstrated in various applications ranging from machine learning, engineering design, and wireless sensor network to environmental modeling. The book provides a comprehensive account of the SCA, including details of the underlying ideas, the modified versions, various applications, and a working MATLAB code for the basic SCA.
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/174397
            Keywords
            Sine Cosine Algorithms; Meta-heuristics; Numerical Optimization; Soft Computing; Numerical Experiments; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence; thema EDItEUR::P Mathematics and Science::PB Mathematics::PBU Optimization; thema EDItEUR::P Mathematics and Science::PB Mathematics::PBK Calculus and mathematical analysis::PBKS Numerical analysis
            DOI
            10.1007/978-981-19-9722-8
            ISBN
            9789811997228
            Publisher
            Springer Nature
            Publisher website
            http://www.springernature.com/oabooks
            Publication date and place
            Singapore, 2023
            Imprint
            Springer Nature Singapore
            Series
            SpringerBriefs in Applied Sciences and Technology; SpringerBriefs in Computational Intelligence,
            Pages
            108
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              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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