Applied Nature-Inspired Computing: Algorithms and Case Studies (Hardcover, 1st ed. 2020)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Series:	Springer Tracts in Nature-Inspired Computing
Release date:	August 2019
First published:	2020
Editors:	Nilanjan Dey • Amira S. Ashour • Siddhartha Bhattacharyya
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	275
Edition:	1st ed. 2020
ISBN-13:	978-981-13-9262-7
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	981-13-9262-5
Barcode:	9789811392627

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