Amira S. (Hrsg.) Ashour
Applied Nature-Inspired Computing: Algorithms and Case Studies
Buch
This book presents a cutting-edge research procedure in the Nature-Inspired Computing (NIC) domain and its connections with computational intelligence areas in real-world engineering applications. It introduces readers to a broad range of algorithms, such as genetic algorithms, particle swarm optimization, the firefly algorithm, flower pollination algorithm, collision-based optimization algorithm, bat algorithm, ant colony optimization, and multi-agent systems. In turn, it provides an overview of meta-heuristic algorithms, comparing the advantages and disadvantages of each. Moreover, the book provides a brief outline of the integration of nat…
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Beschreibung
This book presents a cutting-edge research procedure in the Nature-Inspired Computing (NIC) domain and its connections with computational intelligence areas in real-world engineering applications. It introduces readers to a broad range of algorithms, such as genetic algorithms, particle swarm optimization, the firefly algorithm, flower pollination algorithm, collision-based optimization algorithm, bat algorithm, ant colony optimization, and multi-agent systems. In turn, it provides an overview of meta-heuristic algorithms, comparing the advantages and disadvantages of each. Moreover, the book provides a brief outline of the integration of nature-inspired computing techniques and various computational intelligence paradigms, and highlights nature-inspired computing techniques in a range of applications, including: evolutionary robotics, sports training planning, assessment of water distribution systems, flood simulation and forecasting, traffic control, gene expression analysis, antenna array design, and scheduling/dynamic resource management.
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Produktdetails
Weitere Autoren: Bhattacharyya, Siddhartha (Hrsg.) / Dey, Nilanjan (Hrsg.)
- ISBN: 978-981-1392-65-8
- EAN: 9789811392658
- Produktnummer: 34627358
- Verlag: Springer Singapore
- Sprache: Englisch
- Erscheinungsjahr: 2020
- Seitenangabe: 288 S.
- Masse: H23.5 cm x B15.5 cm x D1.5 cm 441 g
- Auflage: 1st ed. 2020
- Abbildungen: Paperback
- Gewicht: 441
Über den Autor
115943254
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