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Titlebook: Construct, Merge, Solve & Adapt; A Hybrid Metaheurist Christian Blum Book 2024 The Editor(s) (if applicable) and The Author(s), under exclu

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發(fā)表于 2025-3-21 20:07:31 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Construct, Merge, Solve & Adapt
副標(biāo)題A Hybrid Metaheurist
編輯Christian Blum
視頻videohttp://file.papertrans.cn/243/242392/242392.mp4
概述Introduces CMSA: Construct, Merge, Solve & Adapt as combinatorial optimization algorithm.Explains an algorithm combining probabilistic solution construction with an ILP solver.Discusses applications t
叢書(shū)名稱(chēng)Computational Intelligence Methods and Applications
圖書(shū)封面Titlebook: Construct, Merge, Solve & Adapt; A Hybrid Metaheurist Christian Blum Book 2024 The Editor(s) (if applicable) and The Author(s), under exclu
描述.This book describes a general hybrid metaheuristic for combinatorial optimization labeled Construct, Merge, Solve & Adapt (CMSA). The general idea of standard CMSA is the following one. At each iteration, a number of valid solutions to the tackled problem instance are generated in a probabilistic way. Hereby, each of these solutions is composed of a set of solution components. The components found in the generated solutions are then added to an initially empty sub-instance. Next, an exact solver is applied in order to compute the best solution of the sub-instance, which is then used to update the sub-instance provided as input for the next iteration. In this way, the power of exact solvers can be exploited for solving problem instances much too large for a standalone application of the solver...Important research lines on CMSA from recent years are covered in this book. After an introductory chapter about standard CMSA, subsequent chapters cover a self-adaptive CMSA variant as well as a variant equipped with a learning component for improving the quality of the generated solutions over time. Furthermore, on outlining the advantages of using set-covering-based integer linear progra
出版日期Book 2024
關(guān)鍵詞Combinatorial optimization; CMSA; Exact solver; Hybrid algorithms; ILP solver; Knapsack problems; Metaheur
版次1
doihttps://doi.org/10.1007/978-3-031-60103-3
isbn_softcover978-3-031-60105-7
isbn_ebook978-3-031-60103-3Series ISSN 2510-1765 Series E-ISSN 2510-1773
issn_series 2510-1765
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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發(fā)表于 2025-3-21 20:42:02 | 只看該作者
Self-adaptive CMSA,ghlighted its susceptibility to variations in parameter settings. In order to deal with this issue, an innovative self-adaptive variant of the CMSA algorithm, termed ., was developed and will be presented in this chapter. The primary objective is to mitigate the parameter sensitivity that might occu
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Application of CMSA in the Presence of Non-binary Variables,atorial optimization problems that can be expressed through binary integer linear programming (ILP) formulations. Such problems represent an ideal scenario for CMSA, as sub-instances can be easily defined by fixing specific decision variables to certain values or excluding them altogether from the m
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發(fā)表于 2025-3-22 14:33:34 | 只看該作者
Additional Research Lines Concerning CMSA,a brief overview of research directions related to CMSA that have either received limited exploration so far or are presently under investigation. Specifically, it introduces a general CMSA approach for binary integer linear programming models. In this context, CMSA is employed to address integer li
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2510-1765 ion construction with an ILP solver.Discusses applications t.This book describes a general hybrid metaheuristic for combinatorial optimization labeled Construct, Merge, Solve & Adapt (CMSA). The general idea of standard CMSA is the following one. At each iteration, a number of valid solutions to the
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How Mercantifers Emerge and Functiond instead of an integer linear programming solver for sub-instance solving. Following an examination that elucidates the relationship between large neighborhood search and CMSA, the chapter wraps up by underscoring promising avenues for future research.
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