Construct, Merge, Solve & Adapt : A Hybrid Metaheuristic for Combinatorial Optimization / / by Christian Blum
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| Autore: |
Blum Christian
|
| Titolo: |
Construct, Merge, Solve & Adapt : A Hybrid Metaheuristic for Combinatorial Optimization / / by Christian Blum
|
| Pubblicazione: | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Edizione: | 1st ed. 2024. |
| Descrizione fisica: | 1 online resource (202 pages) |
| Disciplina: | 006.3 |
| Soggetto topico: | Artificial intelligence |
| Computational intelligence | |
| Computer science | |
| Operations research | |
| Management science | |
| Computer simulation | |
| Artificial Intelligence | |
| Computational Intelligence | |
| Theory of Computation | |
| Operations Research, Management Science | |
| Computer Modelling | |
| Optimització combinatòria | |
| Nota di contenuto: | Introduction to CMSA -- Self-Adaptive CMSA -- Adding Learning to CMSA -- Replacing Hard Mathematical Models with Set Covering Formulations -- Application of CMSA in the Presence of Non-Binary Variables -- Additional Research Lines Concerning CMSA. |
| Sommario/riassunto: | 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 programming models for sub-instance solving, the author shows how to apply CMSA to problems naturally modelled by non-binary integer linear programming models. The book concludes with a chapter on topics such as the development of a problem-agnostic CMSA and the relation between large neighborhood search and CMSA. Combinatorial optimization problems used in the book as test cases include the minimum dominating set problem, the variable-sized bin packing problem, and an electric vehicle routing problem. The book will be valuable and is intended for researchers, professionals and graduate students working in a wide range of fields, such as combinatorial optimization, algorithmics, metaheuristics, mathematical modeling, evolutionary computing, operations research, artificial intelligence, or statistics. |
| Titolo autorizzato: | Construct, merge, solve & adapt |
| ISBN: | 9783031601033 |
| Formato: | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9910866571103321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |
Serie:
Computational Intelligence Methods and Applications, . 2510-1773