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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:
  • Engelska
  • ISBN:
  • 9783031075148
  • Format:
  • Häftad
  • Sidor:
  • 292
  • Utgiven:
  • 2. september 2023
  • Utgåva:
  • 23001
  • Mått:
  • 155x16x235 mm.
  • Vikt:
  • 446 g.
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Leveranstid: 2-4 veckor
Förväntad leverans: 29. januari 2025

Beskrivning 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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