Kumar / Davim | Optimization Using Evolutionary Algorithms and Metaheuristics | Buch | 978-0-367-77931-3 | sack.de

Buch, Englisch, 148 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 240 g

Reihe: Science, Technology, and Management

Kumar / Davim

Optimization Using Evolutionary Algorithms and Metaheuristics

Applications in Engineering
1. Auflage 2021
ISBN: 978-0-367-77931-3
Verlag: CRC Press

Applications in Engineering

Buch, Englisch, 148 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 240 g

Reihe: Science, Technology, and Management

ISBN: 978-0-367-77931-3
Verlag: CRC Press


Metaheuristic optimization is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity. This is usually applied when two or more objectives are to be optimized simultaneously.

This book is presented with two major objectives. Firstly, it features chapters by eminent researchers in the field providing the readers about the current status of the subject. Secondly, algorithm-based optimization or advanced optimization techniques, which are applied to mostly non-engineering problems, are applied to engineering problems. This book will also serve as an aid to both research and industry. Usage of these methodologies would enable the improvement in engineering and manufacturing technology and support an organization in this era of low product life cycle.

Features:

- Covers the application of recent and new algorithms

- Focuses on the development aspects such as including surrogate modeling, parallelization, game theory, and hybridization

- Presents the advances of engineering applications for both single-objective and multi-objective optimization problems

- Offers recent developments from a variety of engineering fields

- Discusses Optimization using Evolutionary Algorithms and Metaheuristics applications in engineering

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Weitere Infos & Material


Part I: Optimization. 1. Foundations and Principles. 2. Neural Networks. 3. Fuzzy Systems. 4. Evolutionary Computation. 5. Rough Sets. Part II: Applications of Optimization. 6. Mechanical Engineering. 7. Power Control and Optimization. 8. Nanoscience and Nanoengineering. 9. Mining Engineering. 10. Signal Processing. 11. Civil Engineering. 12. Optical Engineering. 13. Bioinformatics and Biomedical Engineering. 14. Ecology and Environmental Engineering.



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