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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 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advances in optimization algorithms for multidisciplinary engineering applications/ edited by Diego Oliva ... [et al.].
Reminder of title:	from classical methods to AI-enhanced solutions /
other author:	Oliva, Diego.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xi, 806 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Metaheuristics theory and applications -- Machine learning -- Engineering applications.
Contained By:	Springer Nature eBook
Subject:	Mathematical optimization. -
Online resource:	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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505 0 $a Metaheuristics theory and applications -- Machine learning -- Engineering applications.
520 $a 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.
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