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Engineering applications of modern m...

Akan, Taymaz.

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Engineering applications of modern metaheuristics

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Engineering applications of modern metaheuristics/ edited by Taymaz Akan ... [et al.].
其他作者:	Akan, Taymaz.
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	vi, 209 p. :ill., digital ;24 cm.
內容註:	Empirical Comparison of Heuristic Optimisation Methods for Automated Car Setup -- Metaheuristic algorithms in IoT: Optimized Edge Node Localization -- Jaya algorithm versus differential evolution: a comparative case study on optic disc localization in eye fundus images -- Minimum transmission power control for the Internet of Things with swarm intelligence algorithms -- An Enhanced Gradient Based Optimized Controller for Load Frequency Control of a Two Area Automatic Generation Control System -- A meta-heuristic algorithm based on the happiness model -- Application of Metaheuristic Techniques for Enhancing the Financial Profitability of Wind Power Generation Systems -- Optimization of Demand Response -- Fitting curves of ruminal degradation using a metaheuristic approach -- Optimizing a Real Case Assembly Line Balancing Problem Using Various Techniques -- Multi-Circle Detection Using Multimodal Optimization.
Contained By:	Springer Nature eBook
標題:	Metaheuristics. -
電子資源:	https://doi.org/10.1007/978-3-031-16832-1
ISBN:	9783031168321

Engineering applications of modern metaheuristics
Engineering applications of modern metaheuristics[electronic resource] /edited by Taymaz Akan ... [et al.]. - Cham :Springer International Publishing :2023. - vi, 209 p. :ill., digital ;24 cm. - Studies in computational intelligence,v. 10691860-9503 ;. - Studies in computational intelligence ;v. 1069..

Empirical Comparison of Heuristic Optimisation Methods for Automated Car Setup -- Metaheuristic algorithms in IoT: Optimized Edge Node Localization -- Jaya algorithm versus differential evolution: a comparative case study on optic disc localization in eye fundus images -- Minimum transmission power control for the Internet of Things with swarm intelligence algorithms -- An Enhanced Gradient Based Optimized Controller for Load Frequency Control of a Two Area Automatic Generation Control System -- A meta-heuristic algorithm based on the happiness model -- Application of Metaheuristic Techniques for Enhancing the Financial Profitability of Wind Power Generation Systems -- Optimization of Demand Response -- Fitting curves of ruminal degradation using a metaheuristic approach -- Optimizing a Real Case Assembly Line Balancing Problem Using Various Techniques -- Multi-Circle Detection Using Multimodal Optimization.

This book is a collection of various methodologies that make it possible for metaheuristics and hyper-heuristics to solve problems that occur in the real world. This book contains chapters that make use of metaheuristics techniques. The application fields range from image processing to transmission power control, and case studies and literature reviews are included to assist the reader. Furthermore, some chapters present cutting-edge methods for load frequency control and IoT implementations. In this sense, the book offers both theoretical and practical contents in the form of metaheuristic algorithms. The researchers used several stochastic optimization methods in this book, including evolutionary algorithms and Swarm-based algorithms. The chapters were written from a scientific standpoint. As a result, the book is primarily aimed at undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics, but it can also be used in courses on Artificial Intelligence, among other things. Similarly, the material may be beneficial to research in evolutionary computation and artificial intelligence communities.

ISBN: 9783031168321

Standard No.: 10.1007/978-3-031-16832-1doiSubjects--Topical Terms:

2206834
Metaheuristics.

LC Class. No.: QA76.9.A43

Dewey Class. No.: 005.13

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520 $a This book is a collection of various methodologies that make it possible for metaheuristics and hyper-heuristics to solve problems that occur in the real world. This book contains chapters that make use of metaheuristics techniques. The application fields range from image processing to transmission power control, and case studies and literature reviews are included to assist the reader. Furthermore, some chapters present cutting-edge methods for load frequency control and IoT implementations. In this sense, the book offers both theoretical and practical contents in the form of metaheuristic algorithms. The researchers used several stochastic optimization methods in this book, including evolutionary algorithms and Swarm-based algorithms. The chapters were written from a scientific standpoint. As a result, the book is primarily aimed at undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics, but it can also be used in courses on Artificial Intelligence, among other things. Similarly, the material may be beneficial to research in evolutionary computation and artificial intelligence communities.
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