Advanced Optimization by Nature-Inspired Algorithms

Softcover Nachdruck of the original 1. Auflage 2018
ISBN: 978-981-1353-45-1
Verlag: Springer Nature Singapore

Buch, Englisch, Band 720, 159 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 277 g

Reihe: Studies in Computational Intelligence

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.

Zielgruppe

Graduate

Autoren/Hrsg.

Bozorg-Haddad, Omid

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde Statik, Dynamik, Kinetik, Kinematik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung
• Technische Wissenschaften Technik Allgemein Bionik, Biomimetik
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle

Inhaltsverzeichnis

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).

Über Autor(innen)

Dr. Omid Bozorg-Haddad is a Professor at the department of irrigation and reclamation engineering, University of Tehran, Iran. His teaching and research interests include water resources and environmental systems analysis, planning, and management as well as application of optimization algorithms in water related systems. He has published more than 100 articles in peer reviewed journals and 100 papers in conference proceedings. He has also supervised more than 50 M.Sc. and Ph.D. students.

Prof. Hugo Loaiciga served as the Water Commissioner for the City of Santa Barbara for six years before joining the Department in 1988. He received the 2002 Service to the Profession Award from the American Society of Civil Engineers and the Environmental and Water Resources Institute for his "longstanding contributions to research and technical activities" of the two groups, and he was elected a Fellow of the American Society of Civil Engineers for his "outstanding contributions to the planning, analysis, and operation of water resources engineering" in 2007.