Advanced Optimization by Nature-Inspired Algorithms [electronic resource] /
Contributor(s): Bozorg-Haddad, Omid [editor.] | SpringerLink (Online service).
Series: Studies in Computational Intelligence: 720Publisher: Singapore : Springer Singapore : Imprint: Springer, 2018Edition: 1st ed. 2018.Description: XV, 159 p. 34 illus., 4 illus. in color. | Binding - Card Paper |.Content type: text Media type: computer Carrier type: online resourceISBN: 9789811052217.Subject(s): Computer Engineering
| Artificial Intelligence | Operations Research/Decision Theory | Theoretical and Applied Mechanics | Computer Imaging, Vision, Pattern Recognition and GraphicsDDC classification: 006.3 Online resources: Click here to access eBook in Springer Nature platform. (Within Campus only.)
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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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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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