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Titlebook: Handbook of Nature-Inspired Optimization Algorithms: The State of the Art; Volume I: Solving Si Ali Mohamed,Diego Oliva,Ponnuthurai Nagarat

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發(fā)表于 2025-3-21 16:23:40 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Handbook of Nature-Inspired Optimization Algorithms: The State of the Art
副標(biāo)題Volume I: Solving Si
編輯Ali Mohamed,Diego Oliva,Ponnuthurai Nagaratnam Sug
視頻videohttp://file.papertrans.cn/422/421731/421731.mp4
概述Explains the algorithms used, selected problems, and the implementation.Focuses on solving single objective bound-constrained real parameter numerical optimization problems with NIOAs.Provides practic
叢書名稱Studies in Systems, Decision and Control
圖書封面Titlebook: Handbook of Nature-Inspired Optimization Algorithms: The State of the Art; Volume I: Solving Si Ali Mohamed,Diego Oliva,Ponnuthurai Nagarat
描述.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.
出版日期Book 2022
關(guān)鍵詞Nature-Inspired Optimization Algorithms; NIOAs; Optimization; Metaheuristics; Evolutionary Algorithms
版次1
doihttps://doi.org/10.1007/978-3-031-07512-4
isbn_softcover978-3-031-07514-8
isbn_ebook978-3-031-07512-4Series ISSN 2198-4182 Series E-ISSN 2198-4190
issn_series 2198-4182
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Book 2022ex 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 difficultie
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Chaotic-SCA Salp Swarm Algorithm Enhanced with Opposition Based Learning: Application to Decrease Cunctions. The experimental results show that the performance of CSOSSA is better than or comparable with the SSA and other meta-heuristic algorithms. To show the applicability of the algorithm in real-world problems, then CSOSSA is applied to the green patient flow problem to minimize the total carbon emitted during the care process.
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2198-4182 numerical optimization problems with NIOAs.Provides practic.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 t
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Khaled Hossameldin Sadek Ibrahim,Ali Khater Mohamed,Ahmed Faroukeitgeber, die für die Einstellung von Dipl.-Mathematikern in Frage kamen, zu informieren und ihren Kreis m?glichst zu erweitern, 2. zu ermitteln, was vom Dipl.-Mathematiker in der Praxis erwartet wird und die Ausbildung unter diesem Gesichtspunkt zu überprüfen.
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