Author: Ali Mohamed
Publisher: Springer Nature
Published: 2022-08-31
Total Pages: 282
ISBN-13: 3031075129
DOWNLOAD EBOOKThe 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.
Book Synopsis Handbook of Nature-Inspired Optimization Algorithms: The State of the Art by : Ali Mohamed
Download or read book Handbook of Nature-Inspired Optimization Algorithms: The State of the Art written by Ali Mohamed and published by Springer Nature. This book was released on 2022-08-31 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.