Handbook of Nature-inspired Optimization Algorithms - the State of the Art: Solving Single Objective Bound-constrained Real-parameter Numerical Optimization Problems (1) - Rilegato

Rilegato

ISBN 10: 3031075110 ISBN 13: 9783031075117

Casa editrice: Springer Nature, 2022

Riassunto

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.

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Dalla quarta di copertina

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.

Dettagli dell?edizione

• Editore: Springer Nature
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 3031075110
• ISBN 13: 9783031075117
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 292
• Redattore: Mohamed Ali, Oliva Diego, Suganthan Ponnuthurai Nagaratnam
• Contatto del produttore: non disponibile