Handbook of Nature-Inspired Optimization Algorithms: The State of the Art
Volume I: Solving Single Objective Bound-Constrained Real-Parameter Numerical Optimization Problems
Published on 1. September 2022
Book
Hardback
X, 279 pages
978-3-031-07511-7 (ISBN)
Description
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.More details
Series
Edition
2022 ed.
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
21 s/w Abbildungen, 73 farbige Abbildungen
X, 279 p. 94 illus., 73 illus. in color.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 22 mm
Weight
606 gr
ISBN-13
978-3-031-07511-7 (9783031075117)
DOI
10.1007/978-3-031-07512-4
Schweitzer Classification
Other editions
Additional editions
Ali Mohamed | Diego Oliva | Ponnuthurai Nagaratnam Suganthan
Handbook of Nature-Inspired Optimization Algorithms: The State of the Art
Volume I: Solving Single Objective Bound-Constrained Real-Parameter Numerical Optimization Problems
Book
09/2023
Springer
€160.49
Shipment within 15-20 days
Ali Mohamed | Diego Oliva | Ponnuthurai Nagaratnam Suganthan
Handbook of Nature-Inspired Optimization Algorithms: The State of the Art
Volume I: Solving Single Objective Bound-Constrained Real-Parameter Numerical Optimization Problems
E-Book
08/2022
1st Edition
Springer
€149.79
Available for download
Content
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.