Handbook of Nature-Inspired Optimization Algorithms: The State of the Art : Volume I: Solving Single Objective Bound-Constrained Real-Parameter Numerical Optimization Problems / edited by Ali Mohamed, Diego Oliva, Ponnuthurai Nagaratnam Suganthan

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Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Studies in Systems, Decision and Control ; 212Verlag: Cham : Springer International Publishing, 2022Verlag: Cham : Imprint: Springer, 2022Auflage: 1st ed. 2022Beschreibung: 1 Online-Ressource(X, 279 p. 94 illus., 73 illus. in color.)ISBN:

9783031075124

Schlagwörter:

Andere physische Formen: 9783031075117 | 9783031075131 | 9783031075148 | Erscheint auch als: 9783031075117 Druck-Ausgabe | Erscheint auch als: 9783031075131 Druck-Ausgabe | Erscheint auch als: 9783031075148 Druck-Ausgabe | Erscheint auch als: Handbook of nature-inspired optimization algorithms: the state of the art ; Volume 1: Solving single objective bound-constrained real-parameter numerical optimization problems. Druck-Ausgabe. Cham : Springer, 2022. x, 279 SeitenRVK: RVK: SK 870DOI: DOI: 10.1007/978-3-031-07512-4Online-Ressourcen:

Zusammenfassung: 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.Zusammenfassung: 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.PPN: PPN: 181585944XPackage identifier: Produktsigel: ZDB-2-SEB | ZDB-2-INR | ZDB-2-SXIT

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