04548nam a22006135i 4500 978-3-031-60103-3 DE-He213 20251030112320.0 cr nn 008mamaa 240619s2024 sz | s |||| 0|eng d 9783031601033 978-3-031-60103-3 10.1007/978-3-031-60103-3 doi Q334-342 TA347.A78 UYQ bicssc COM004000 bisacsh UYQ thema Blum, Christian. author. aut http://id.loc.gov/vocabulary/relators/aut 78948 Construct, Merge, Solve & Adapt [electronic resource] : A Hybrid Metaheuristic for Combinatorial Optimization / by Christian Blum. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XVI, 192 p. 58 illus., 43 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Computational Intelligence Methods and Applications, 2510-1773 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. 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. Artificial intelligence. 58441 Computational intelligence. 64052 Computer science. 58552 Operations research. 57943 Management science. 57941 Computer simulation. Artificial Intelligence. 58441 Computational Intelligence. 64052 Theory of Computation. 67552 Operations Research, Management Science. 65527 Computer Modelling. 65738 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031601026 Printed edition: 9783031601040 Printed edition: 9783031601057 Computational Intelligence Methods and Applications, 2510-1773 70583 https://doi.org/10.1007/978-3-031-60103-3 ZDB-2-SCS ZDB-2-SXCS 60841 60841