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oapen-20.500.12657-613292024-03-27T14:14:29Z Sine Cosine Algorithm for Optimization Bansal, Jagdish Chand Bajpai, Prathu Rawat, Anjali Nagar, Atulya K. 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 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. 2023-02-13T17:28:26Z 2023-02-13T17:28:26Z 2023 book ONIX_20230213_9789811997228_63 9789811997228 https://library.oapen.org/handle/20.500.12657/61329 eng SpringerBriefs in Applied Sciences and Technology; SpringerBriefs in Computational Intelligence application/pdf n/a 978-981-19-9722-8.pdf https://link.springer.com/978-981-19-9722-8 Springer Nature Springer Nature Singapore 10.1007/978-981-19-9722-8 10.1007/978-981-19-9722-8 6c6992af-b843-4f46-859c-f6e9998e40d5 71081f8c-296f-423e-a929-8274a9a2e4f0 9789811997228 Springer Nature Singapore 108 Singapore [...] open access
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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.
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