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Metaheuristic optimizationnature-ins...

Okwu, Modestus O.

Metaheuristic optimizationnature-inspired algorithms swarm and computational intelligence, theory and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Metaheuristic optimizationby Modestus O. Okwu, Lagouge K. Tartibu.
Reminder of title:	nature-inspired algorithms swarm and computational intelligence, theory and applications /
Author:	Okwu, Modestus O.
other author:	Tartibu, Lagouge K.
Published:	Cham :Springer International Publishing :2021.
Description:	xii, 192 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Metaheuristics.
Online resource:	https://doi.org/10.1007/978-3-030-61111-8
ISBN:	9783030611118$q(electronic bk.)

Metaheuristic optimizationnature-inspired algorithms swarm and computational intelligence, theory and applications /
Okwu, Modestus O.

Metaheuristic optimizationnature-inspired algorithms swarm and computational intelligence, theory and applications /[electronic resource] :by Modestus O. Okwu, Lagouge K. Tartibu. - Cham :Springer International Publishing :2021. - xii, 192 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.9271860-949X ;. - Studies in computational intelligence ;v. 216..

Introduction To Optimization -- Particle Swarm Optimisation -- Artificial Bee Colony Algorithm -- Ant Colony Algorithm -- Grey Wolf Optimizer -- Whale Optimization Algorithm -- Bat Algorithm -- Ant Lion Optimization Algorithm -- Grasshopper Optimisation Algorithm (Goa) -- Moths-Flame Optimization Algorithm -- Genetic Algorithm -- Artificial Neural Network -- Future of Nature Inspired Algorithm, Swarm and Computational Intelligence.

This book exemplifies how algorithms are developed by mimicking nature. Classical techniques for solving day-to-day problems is time-consuming and cannot address complex problems. Metaheuristic algorithms are nature-inspired optimization techniques for solving real-life complex problems. This book emphasizes the social behaviour of insects, animals and other natural entities, in terms of converging power and benefits. Major nature-inspired algorithms discussed in this book include the bee colony algorithm, ant colony algorithm, grey wolf optimization algorithm, whale optimization algorithm, firefly algorithm, bat algorithm, ant lion optimization algorithm, grasshopper optimization algorithm, butterfly optimization algorithm and others. The algorithms have been arranged in chapters to help readers gain better insight into nature-inspired systems and swarm intelligence. All the MATLAB codes have been provided in the appendices of the book to enable readers practice how to solve examples included in all sections. This book is for experts in Engineering and Applied Sciences, Natural and Formal Sciences, Economics, Humanities and Social Sciences.

ISBN: 9783030611118$q(electronic bk.)

Standard No.: 10.1007/978-3-030-61111-8doiSubjects--Topical Terms:

757062
Metaheuristics.

LC Class. No.: QA76.9.A43 / O58 2021

Dewey Class. No.: 006.3

Metaheuristic optimizationnature-inspired algorithms swarm and computational intelligence, theory and applications /
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505 0 $a Introduction To Optimization -- Particle Swarm Optimisation -- Artificial Bee Colony Algorithm -- Ant Colony Algorithm -- Grey Wolf Optimizer -- Whale Optimization Algorithm -- Bat Algorithm -- Ant Lion Optimization Algorithm -- Grasshopper Optimisation Algorithm (Goa) -- Moths-Flame Optimization Algorithm -- Genetic Algorithm -- Artificial Neural Network -- Future of Nature Inspired Algorithm, Swarm and Computational Intelligence.
520 $a This book exemplifies how algorithms are developed by mimicking nature. Classical techniques for solving day-to-day problems is time-consuming and cannot address complex problems. Metaheuristic algorithms are nature-inspired optimization techniques for solving real-life complex problems. This book emphasizes the social behaviour of insects, animals and other natural entities, in terms of converging power and benefits. Major nature-inspired algorithms discussed in this book include the bee colony algorithm, ant colony algorithm, grey wolf optimization algorithm, whale optimization algorithm, firefly algorithm, bat algorithm, ant lion optimization algorithm, grasshopper optimization algorithm, butterfly optimization algorithm and others. The algorithms have been arranged in chapters to help readers gain better insight into nature-inspired systems and swarm intelligence. All the MATLAB codes have been provided in the appendices of the book to enable readers practice how to solve examples included in all sections. This book is for experts in Engineering and Applied Sciences, Natural and Formal Sciences, Economics, Humanities and Social Sciences.
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