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Engineering Applications of Modern Metaheuristics (Studies in Computational Intelligence, nr. 1069)

Editat de Taymaz Akan, Ahmed M. Anter, A. Şima Etaner-Uyar, Diego Oliva
Notă GoodReads:
en Limba Engleză Hardback – 20 Jan 2023
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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Specificații

ISBN-13: 9783031168314
ISBN-10: 3031168313
Ilustrații: VI, 209 p. 96 illus., 70 illus. in color.
Dimensiuni: 155 x 235 mm
Ediția: 1st ed. 2023
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Computational Intelligence

Locul publicării: Cham, Switzerland

Cuprins

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.

Textul de pe ultima copertă

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.

Caracteristici

Provides the reader with the most representative optimization tools used for scientific and engineering problems
Explains the algorithms used, the selected problem, and the implementation
Provides practical examples, comparisons, and experimental results