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Image of Nature-Inspired Metaheuristic Algorithms for Engineering Optimization Applications

Electronic Resource

Nature-Inspired Metaheuristic Algorithms for Engineering Optimization Applications

Carbas, Serdar - Personal Name; Toktas, Abdurrahim - Personal Name; Ustun, Deniz - Personal Name;

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This book engages in an ongoing topic, such as the implementation of nature-inspired metaheuristic algorithms, with a main concentration on optimization problems in different fields of engineering optimization applications. The chapters of the book provide concise overviews of various nature-inspired metaheuristic algorithms, defining their profits in obtaining the optimal solutions of tiresome engineering design problems that cannot be efficiently resolved via conventional mathematical-based techniques. Thus, the chapters report on advanced studies on the applications of not only the traditional, but also the contemporary certain nature-inspired metaheuristic algorithms to specific engineering optimization problems with single and multi-objectives. Harmony search, artificial bee colony, teaching learning-based optimization, electrostatic discharge, grasshopper, backtracking search, and interactive search are just some of the methods exhibited and consulted step by step in application contexts. The book is a perfect guide for graduate students, researchers, academicians, and professionals willing to use metaheuristic algorithms in engineering optimization applications.


Availability
Inventory Code Barcode Call Number Location Status
1401001660EB0001170005.13 NatCentral LibraryAvailable
Detail Information
Series Title
-
Call Number
005.13 Nat
Publisher
Singapore : Springer Singapore., 2022
Collation
XII, 404p.:Ill
Language
English
ISBN/ISSN
978-981-33-6773-9
Classification
005.13
Content Type
Ebook
Media Type
-
Carrier Type
online resource
Edition
-
Subject(s)
Metaheuristics
Specific Detail Info
-
Statement of Responsibility
BRF
Other version/related

No other version available

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  • Nature-Inspired Metaheuristic Algorithms for Engineering Optimization Applications
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