Hardcover. 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 serveas a reference for relevantcourses in computer science, artificial intelligence and machine learning, naturalcomputation, engineering optimization and data mining. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Bestandsnummer des Verk�ufers 9783319302331

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Bibliografische Details

Titel: Nature-Inspired Computation in Engineering (Hardcover)
Verlag: Springer International Publishing AG, Cham
Erscheinungsjahr: 2016
Sprache: Englisch
ISBN-10: 3319302337
ISBN-13: 9783319302331
Einband: Hardcover
Auflage: 1. Auflage
Zustand: new

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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 serve as a reference for relevant courses in computer science, artificial intelligence and machine learning, natural computation, engineering optimization and data mining.

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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.

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