Mohamed / Oliva / Suganthan | Handbook of Nature-Inspired Optimization Algorithms: The State of the Art | E-Book | sack.de
E-Book

E-Book, Englisch, Band 212, 279 Seiten, eBook

Reihe: Studies in Systems, Decision and Control

Mohamed / Oliva / Suganthan Handbook of Nature-Inspired Optimization Algorithms: The State of the Art

Volume I: Solving Single Objective Bound-Constrained Real-Parameter Numerical Optimization Problems
1. Auflage 2022
ISBN: 978-3-031-07512-4
Verlag: Springer International Publishing
Format: PDF
 Kopierschutz: 1 - PDF Watermark

Volume I: Solving Single Objective Bound-Constrained Real-Parameter Numerical Optimization Problems

E-Book, Englisch, Band 212, 279 Seiten, eBook

Reihe: Studies in Systems, Decision and Control

ISBN: 978-3-031-07512-4
Verlag: Springer International Publishing
Format: PDF
 Kopierschutz: 1 - PDF Watermark

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.
Mohamed / Oliva / Suganthan Handbook of Nature-Inspired Optimization Algorithms: The State of the Art jetzt bestellen!

Zielgruppe

Research

Autoren/Hrsg.

Mohamed, Ali
Oliva, Diego
Suganthan, Ponnuthurai Nagaratnam

Weitere Infos & Material

Chaotic-SCA Salp Swarm Algorithm Enhanced with Opposition Based Learning:  Application to Decrease Carbon Footprint in Patient Flow.- Design and Performance Evaluation of Objective Functions Based on Various Measures of Fuzzy Entropies for Image Segmentation using Grey Wolf Optimization.- Improved Artificial Bee Colony Algorithm with Adaptive Pursuit Based Strategy Selection.- Beetle Antennae Search Algorithm for the Motion Planning of Industrial Manipulator.- Solving Optimal Power Flow with Considering Placement of TCSC and FACTS Cost Using Cuckoo Search Algorithm.

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