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Handbook of Nature-Inspired Optimization Algorithms: The State of the Art: Volume I: Solving Single Objective Bound-Constrained Real-Parameter ... in Systems, Decision and Control, 212) - Hardcover
- PublisherSpringer
- Publication date2022
- ISBN 10 3031075110
- ISBN 13 9783031075117
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages289
- EditorMohamed Ali, Oliva Diego, Suganthan Ponnuthurai Nagaratnam
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Handbook of Nature-Inspired Optimization Algorithms: The State of the Art: Volume I: Solving Single Objective Bound-Constrained Real-Parameter ... in Systems, Decision and Control, 212)
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Handbook of Nature-Inspired Optimization Algorithms: The State of the Art : Volume I: Solving Single Objective Bound-Constrained Real-Parameter Numerical Optimization Problems
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ISBN 10: 3031075110
ISBN 13: 9783031075117
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Handbook of Nature-Inspired Optimization Algorithms: The State of the Art: Volume I: Solving Single Objective Bound-Constrained Real-Parameter ... in Systems, Decision and Control, 212)
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Handbook of Nature-Inspired Optimization Algorithms: The State of the Art
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Springer International Publishing Sep 2022, 2022
ISBN 10: 3031075110
ISBN 13: 9783031075117
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The introduction of nature-inspired optimization algorithms (NIOAs), over the past three decades, helped solve nonlinear, high-dimensional, and complex computational optimization problems. NIOAs have been originally developed to overcome the challenges of global optimization problems such as nonlinearity, non-convexity, non-continuity, non-differentiability, and/or multimodality which traditional numerical optimization techniques had difficulties solving.The main objective for this book is to make available a self-contained collection of modern research addressing the general bound-constrained optimization problems in many real-world applications using nature-inspired optimization algorithms. This book is suitable for a graduate class on optimization, but will also be useful for interested senior students working on their research projects. 292 pp. Englisch. Seller Inventory # 9783031075117
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Handbook of Nature-Inspired Optimization Algorithms: The State of the Art
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ISBN 10: 3031075110
ISBN 13: 9783031075117
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Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The introduction of nature-inspired optimization algorithms (NIOAs), over the past three decades, helped solve nonlinear, high-dimensional, and complex computational optimization problems. NIOAs have been originally developed to overcome the challenges o. Seller Inventory # 585900177
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Handbook of Nature-Inspired Optimization Algorithms: The State of the Art : Volume I: Solving Single Objective Bound-Constrained Real-Parameter Numerical Optimization Problems
Published by
Springer International Publishing, 2022
ISBN 10: 3031075110
ISBN 13: 9783031075117
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - The introduction of nature-inspired optimization algorithms (NIOAs), over the past three decades, helped solve nonlinear, high-dimensional, and complex computational optimization problems. NIOAs have been originally developed to overcome the challenges of global optimization problems such as nonlinearity, non-convexity, non-continuity, non-differentiability, and/or multimodality which traditional numerical optimization techniques had difficulties solving.The main objective for this book is to make available a self-contained collection of modern research addressing the general bound-constrained optimization problems in many real-world applications using nature-inspired optimization algorithms. This book is suitable for a graduate class on optimization, but will also be useful for interested senior students working on their research projects. Seller Inventory # 9783031075117
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Handbook of Nature-Inspired Optimization Algorithms: The State of the Art: Volume I: Solving Single Objective Bound-Constrained Real-Parameter ... in Systems, Decision and Control, 212)
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