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Advances in optimization algorithms ...

Oliva, Diego.

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Advances in optimization algorithms for multidisciplinary engineering applications = from classical methods to AI-enhanced solutions /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advances in optimization algorithms for multidisciplinary engineering applications/ edited by Diego Oliva ... [et al.].
其他題名:	from classical methods to AI-enhanced solutions /
其他作者:	Oliva, Diego.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	xi, 806 p. :ill. (some col.), digital ;24 cm.
內容註:	Metaheuristics theory and applications -- Machine learning -- Engineering applications.
Contained By:	Springer Nature eBook
標題:	Mathematical optimization. -
電子資源:	https://doi.org/10.1007/978-3-031-78440-8
ISBN:	9783031784408

Advances in optimization algorithms for multidisciplinary engineering applications = from classical methods to AI-enhanced solutions /
Advances in optimization algorithms for multidisciplinary engineering applicationsfrom classical methods to AI-enhanced solutions /[electronic resource] :edited by Diego Oliva ... [et al.]. - Cham :Springer Nature Switzerland :2025. - xi, 806 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v. 8061860-9503 ;. - Studies in computational intelligence ;v. 806..

Metaheuristics theory and applications -- Machine learning -- Engineering applications.

This book is an authoritative compilation of the latest advancements in optimization techniques. This book covers a wide array of methods ranging from classical to metaheuristic to AI-enhanced approaches. The chapters are meticulously selected and organized in three sections-metaheuristics, machine learning and engineering applications. This allows for an in-depth exploration of diverse topics ranging from image processing to feature selection to data clustering, to practical applications like energy optimization, smart grids, healthcare diagnostics, etc. Each chapter delves into the specific algorithms and applications as well as provides ample theoretical insights. Accordingly, this book is ideally suited for undergraduate and postgraduate students in fields such as science, engineering and computational mathematics. It is also an invaluable resource for courses on artificial intelligence, computational intelligence, etc. Researchers and professionals in evolutionary computation, artificial intelligence and engineering will find the material especially useful for advancing their work and exploring new frontiers in optimization.

ISBN: 9783031784408

Standard No.: 10.1007/978-3-031-78440-8doiSubjects--Topical Terms:

517763
Mathematical optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

Advances in optimization algorithms for multidisciplinary engineering applications = from classical methods to AI-enhanced solutions /
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