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Titlebook: Matheuristics; Algorithms and Imple Vittorio Maniezzo,Marco Antonio Boschetti,Thomas S Textbook 2021 The Editor(s) (if applicable) and The

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發(fā)表于 2025-3-21 18:23:41 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Matheuristics
副標(biāo)題Algorithms and Imple
編輯Vittorio Maniezzo,Marco Antonio Boschetti,Thomas S
視頻videohttp://file.papertrans.cn/628/627709/627709.mp4
概述Includes over 40 algorithms with associated codes and step-by-step trace of sample runs.Provides detailed presentation of best-known metaheuristics and their mathematical hybrids.Describes both extens
叢書(shū)名稱EURO Advanced Tutorials on Operational Research
圖書(shū)封面Titlebook: Matheuristics; Algorithms and Imple Vittorio Maniezzo,Marco Antonio Boschetti,Thomas S Textbook 2021 The Editor(s) (if applicable) and The
描述.This book is the first comprehensive tutorial on matheuristics. Matheuristics are based on mathematical extensions of previously known heuristics, mainly metaheuristics, and on original, area-specific approaches. This tutorial provides a detailed discussion of both contributions, presenting the pseudocodes of over 40 algorithms, abundant literature references, and for each case a step-by-step description of a sample run on a common Generalized Assignment Problem example. C++ source codes of all algorithms are available in an associated SW repository..
出版日期Textbook 2021
關(guān)鍵詞Combinatorial optimization; Heuristic algorithms; Mathematical optimization; Metaheuristics; Generalized
版次1
doihttps://doi.org/10.1007/978-3-030-70277-9
isbn_softcover978-3-030-70279-3
isbn_ebook978-3-030-70277-9Series ISSN 2364-687X Series E-ISSN 2364-6888
issn_series 2364-687X
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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Fore-and-Backon paid to optimality proofs. A significant characteristic of this method is that, despite being a primal only method, it is able to compute bounds to the cost of completing partial solutions, therefore to discard partial solutions from expansion and ultimately to reduce the search space.
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Kernel Search) of promising decision variables and then to partition the remaining ones into ., which are concatenated one at a time to the kernel in order to check whether improving solutions can be found. An example along these lines is proposed in this chapter for the GAP.
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The Generalized Assignment Problem practical applications and has therefore been intensively studied. We use this problem as a test case example of all algorithms presented in the text. This section reviews the state of the art of research on GAP and shows the application of several mathematical programming techniques on GAP instances.
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