Articoli correlati a Handbook of Nature-inspired Optimization Algorithms...

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

 
9783031075117: Handbook of Nature-inspired Optimization Algorithms - the State of the Art: Solving Single Objective Bound-constrained Real-parameter Numerical Optimization Problems (1)

Sinossi

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.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.