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Advanced Optimization by Nature-Inspired Algorithms [electronic resource] / edited by Omid Bozorg-Haddad.

Contributor(s): Bozorg-Haddad, Omid [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Computational Intelligence: 720Publisher: Singapore : Springer Nature Singapore : Imprint: Springer, 2018Edition: 1st ed. 2018.Description: XV, 159 p. 34 illus., 4 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9789811052217.Subject(s): Computational intelligence | Mathematical optimization | Artificial intelligence | Operations research | Mechanics, Applied | Image processing—Digital techniques | Computer vision | Computational Intelligence | Optimization | Artificial Intelligence | Operations Research and Decision Theory | Engineering Mechanics | Computer Imaging, Vision, Pattern Recognition and GraphicsAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
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).
In: Springer Nature eBookSummary: 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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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.

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