Nature-Inspired Computation in Engineering

Xin-She Yang

ISBN 10: 3319807579 / ISBN 13: 9783319807577

Published by Springer International Publishing Apr 2018, 2018

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -This timely review book summarizes thestate-of-the-art developments in nature-inspired optimization algorithms andtheir applications in engineering. Algorithms and topics include the overviewand history of nature-inspired algorithms, discrete firefly algorithm, discretecuckoo search, plant propagation algorithm, parameter-free bat algorithm,gravitational search, biogeography-based algorithm, differential evolution,particle swarm optimization and others. Applications include vehicle routing,swarming robots, discrete and combinatorial optimization, clustering ofwireless sensor networks, cell formation, economic load dispatch, metamodeling,surrogated-assisted cooperative co-evolution, data fitting and reverseengineering as well as other case studies in engineering. This book will be anideal reference for researchers, lecturers, graduates and engineers who areinterested in nature-inspired computation, artificial intelligence andcomputational intelligence. It can also serve as a reference for relevantcourses in computer science, artificial intelligence and machine learning, naturalcomputation, engineering optimization and data mining. 288 pp. Englisch.

Seller Inventory # 9783319807577

Bibliographic Details

• Title: Nature-Inspired Computation in Engineering
• Publisher: Springer International Publishing Apr 2018
• Publication Date: 2018
• Language: English
• ISBN 10: 3319807579
• ISBN 13: 9783319807577
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

This timely review book summarizes the state-of-the-art developments in nature-inspired optimization algorithms and their applications in engineering. Algorithms and topics include the overview and history of nature-inspired algorithms, discrete firefly algorithm, discrete cuckoo search, plant propagation algorithm, parameter-free bat algorithm, gravitational search, biogeography-based algorithm, differential evolution, particle swarm optimization and others. Applications include vehicle routing, swarming robots, discrete and combinatorial optimization, clustering of wireless sensor networks, cell formation, economic load dispatch, metamodeling, surrogated-assisted cooperative co-evolution, data fitting and reverse engineering as well as other case studies in engineering. This book will be an ideal reference for researchers, lecturers, graduates and engineers who are interested in nature-inspired computation, artificial intelligence and computational intelligence. It can also serveas a reference for relevant courses in computer science, artificial intelligence and machine learning, natural computation, engineering optimization and data mining.