Handbook of Nature-Inspired Optimization Algorithms: The State of the Art
Springer International Publishing (Verlag)
978-3-031-07511-7 (ISBN)
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.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.
Erscheinungsdatum | 02.09.2022 |
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Reihe/Serie | Studies in Systems, Decision and Control |
Zusatzinfo | X, 279 p. 94 illus., 73 illus. in color. |
Verlagsort | Cham |
Sprache | englisch |
Maße | 155 x 235 mm |
Gewicht | 595 g |
Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
Technik | |
Schlagworte | evolutionary algorithms • Metaheuristics • Nature-Inspired Optimization Algorithms • NIOAs • Optimization |
ISBN-10 | 3-031-07511-0 / 3031075110 |
ISBN-13 | 978-3-031-07511-7 / 9783031075117 |
Zustand | Neuware |
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