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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https://doi.org/10.1007/978-1-349-05806-8sts in the iterated application of an exact approach—such as, for example, an integer linear programming (ILP) solver—to sub-instances of the original problem instances to be solved. These sub-instances are extended at each iteration by adding solution components from a set of valid solutions that a

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https://doi.org/10.1007/978-1-349-05806-8ghlighted 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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https://doi.org/10.1007/978-1-349-05806-8this book, unsophisticated variants of CMSA are capable of yielding highly satisfactory results. Nevertheless, such basic CMSA variants can, of course, undergo enhancement through the incorporation of supplementary algorithmic components. One avenue for refining barebone CMSA variants involves intro

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Properties and Mechanics of Solids,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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How Mercantifers Emerge and Functiona 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