ISBN:
9783031168321
Sprache:
Englisch
Seiten:
1 Online-Ressource(VI, 209 p. 96 illus., 70 illus. in color.)
Ausgabe:
1st ed. 2023.
Serie:
Studies in Computational Intelligence 1069
Paralleltitel:
Erscheint auch als
Paralleltitel:
Erscheint auch als
Paralleltitel:
Erscheint auch als
Schlagwort(e):
Engineering—Data processing.
;
Computational intelligence.
;
Artificial intelligence.
;
Engineering
Kurzfassung:
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.
Kurzfassung:
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.
DOI:
10.1007/978-3-031-16832-1
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