Construct, merge, solve & adapt : a hybrid metaheuristic for combinatorial optimization / / Christian Blum

• Autore: Blum Christian
• Pubblicazione: Cham : , : Springer Nature Switzerland, , [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
• Serie: Computational intelligence methods and applications, . 2510-1773