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020 _a9783031601033
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024 7 _a10.1007/978-3-031-60103-3
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
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072 7 _aCOM004000
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100 1 _aBlum, Christian.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978948
245 1 0 _aConstruct, Merge, Solve & Adapt
_h[electronic resource] :
_bA Hybrid Metaheuristic for Combinatorial Optimization /
_cby Christian Blum.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXVI, 192 p. 58 illus., 43 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aComputational Intelligence Methods and Applications,
_x2510-1773
505 0 _aIntroduction 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.
520 _aThis 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.
650 0 _aArtificial intelligence.
_958441
650 0 _aComputational intelligence.
_964052
650 0 _aComputer science.
_958552
650 0 _aOperations research.
_957943
650 0 _aManagement science.
_957941
650 0 _aComputer simulation.
650 1 4 _aArtificial Intelligence.
_958441
650 2 4 _aComputational Intelligence.
_964052
650 2 4 _aTheory of Computation.
_967552
650 2 4 _aOperations Research, Management Science.
_965527
650 2 4 _aComputer Modelling.
_965738
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031601026
776 0 8 _iPrinted edition:
_z9783031601040
776 0 8 _iPrinted edition:
_z9783031601057
830 0 _aComputational Intelligence Methods and Applications,
_x2510-1773
_970583
856 4 0 _uhttps://doi.org/10.1007/978-3-031-60103-3
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