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Applied Nature-Inspired Computing: Algorithms and Case Studies
- Publication Year :
- 2020
-
Abstract
- 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.
- Subjects :
- Natural computation
Subjects
Details
- Language :
- English
- ISBNs :
- 9789811392627 and 9789811392634
- Database :
- eBook Index
- Journal :
- Applied Nature-Inspired Computing: Algorithms and Case Studies
- Publication Type :
- eBook
- Accession number :
- 2228903