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Handbook of Nature-Inspired Optimization Algorithms: The State of the Art

Om Handbook of Nature-Inspired Optimization Algorithms: The State of the Art

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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  • Språk:
  • Engelsk
  • ISBN:
  • 9783031075148
  • Bindende:
  • Paperback
  • Sider:
  • 292
  • Utgitt:
  • 2. september 2023
  • Utgave:
  • 23001
  • Dimensjoner:
  • 155x16x235 mm.
  • Vekt:
  • 446 g.
  • BLACK NOVEMBER
  Gratis frakt
Leveringstid: 2-4 uker
Forventet levering: 7. desember 2024

Beskrivelse av Handbook of Nature-Inspired Optimization Algorithms: The State of the Art

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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