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Advanced optimization by nature-insp...

Bozorg-Haddad, Omid.

Advanced optimization by nature-inspired algorithms

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advanced optimization by nature-inspired algorithmsedited by Omid Bozorg-Haddad.
other author:	Bozorg-Haddad, Omid.
Published:	Singapore :Springer Singapore :2018.
Description:	xv, 159 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Natural computation.
Online resource:	http://dx.doi.org/10.1007/978-981-10-5221-7
ISBN:	9789811052217$q(electronic bk.)

Advanced optimization by nature-inspired algorithms
Advanced optimization by nature-inspired algorithms[electronic resource] /edited by Omid Bozorg-Haddad. - Singapore :Springer Singapore :2018. - xv, 159 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.7201860-949X ;. - Studies in computational intelligence ;v. 216..

Introduction -- Cat Swarm Optimization (CSO) Algorithm -- League Championship Algorithm (LCA) -- Anarchic Society Optimization (ASO) Algorithm -- Cuckoo Optimization Algorithm (COA) -- Teaching-Learning-Based Optimization (TLBO) Algorithm -- Flower pollination Algorithm (FPA) -- Krill Herd Algorithm (KHA) -- Grey Wolf Optimization (GWO) Algorithm -- Shark Smell Optimization (SSO) Algorithm -- Ant Lion Optimizer (ALO) Algorithm -- Gradient Evolution (GE) Algorithm -- Moth-Flame Optimization (MFO) Algorithm -- Crow Search Algorithm (CSA) -- Dragonfly Algorithm (DA)

This book, compiles, presents, and explains the most important meta-heuristic and evolutionary optimization algorithms whose successful performance has been proven in different fields of engineering, and it includes application of these algorithms to important engineering optimization problems. In addition, this book guides readers to studies that have implemented these algorithms by providing a literature review on developments and applications of each algorithm. This book is intended for students, but can be used by researchers and professionals in the area of engineering optimization.

ISBN: 9789811052217$q(electronic bk.)

Standard No.: 10.1007/978-981-10-5221-7doiSubjects--Topical Terms:

339160
Natural computation.

LC Class. No.: QA76.9.N37

Dewey Class. No.: 006.38

Advanced optimization by nature-inspired algorithms
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245 0 0 $a Advanced optimization by nature-inspired algorithms $h [electronic resource] / $c edited by Omid Bozorg-Haddad.
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300 $a xv, 159 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.720
505 0 $a Introduction -- Cat Swarm Optimization (CSO) Algorithm -- League Championship Algorithm (LCA) -- Anarchic Society Optimization (ASO) Algorithm -- Cuckoo Optimization Algorithm (COA) -- Teaching-Learning-Based Optimization (TLBO) Algorithm -- Flower pollination Algorithm (FPA) -- Krill Herd Algorithm (KHA) -- Grey Wolf Optimization (GWO) Algorithm -- Shark Smell Optimization (SSO) Algorithm -- Ant Lion Optimizer (ALO) Algorithm -- Gradient Evolution (GE) Algorithm -- Moth-Flame Optimization (MFO) Algorithm -- Crow Search Algorithm (CSA) -- Dragonfly Algorithm (DA)
520 $a This book, compiles, presents, and explains the most important meta-heuristic and evolutionary optimization algorithms whose successful performance has been proven in different fields of engineering, and it includes application of these algorithms to important engineering optimization problems. In addition, this book guides readers to studies that have implemented these algorithms by providing a literature review on developments and applications of each algorithm. This book is intended for students, but can be used by researchers and professionals in the area of engineering optimization.
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