{"product_id":"9783031601057","title":"Computational Intelligence Methods and Applications: A Hybrid Metaheuristic for Combinatorial Optimization","description":"\u003ch1\u003eComputational Intelligence Methods and Applications: A Hybrid Metaheuristic for Combinatorial Optimization\u003c\/h1\u003e \u003ch2\u003eBlum, Christian\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis book describes a general hybrid metaheuristic for combinatorial optimization labeled Construct, Merge, Solve \u0026amp; 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.\u003c\/p\u003e\n\n\u003cp\u003eImportant 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.\u003c\/p\u003e\n\n\u003cp\u003eThe 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.\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 2025-06-20\u003c\/p\u003e \u003cp\u003eFormat: Paperback\u003c\/p\u003e \u003cp\u003eISBN-13: 9783031601057\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/978-3-031-60103-3\u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 192\u003c\/p\u003e ","brand":"Springer Nature Switzerland","offers":[{"title":"Default Title","offer_id":44421991792780,"sku":"9783031601057","price":152.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783031601057.jpg?v=1774531954","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9783031601057","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}