LEADER 04522nam 22006975 450 001 9910866571103321 005 20241218104521.0 010 $a9783031601033 024 7 $a10.1007/978-3-031-60103-3 035 $a(CKB)32317225700041 035 $a(MiAaPQ)EBC31496593 035 $a(Au-PeEL)EBL31496593 035 $a(DE-He213)978-3-031-60103-3 035 $a(EXLCZ)9932317225700041 100 $a20240619d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aConstruct, Merge, Solve & Adapt $eA Hybrid Metaheuristic for Combinatorial Optimization /$fby Christian Blum 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (202 pages) 225 1 $aComputational Intelligence Methods and Applications,$x2510-1773 311 08$a9783031601026 327 $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. 330 $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. 410 0$aComputational Intelligence Methods and Applications,$x2510-1773 606 $aArtificial intelligence 606 $aComputational intelligence 606 $aComputer science 606 $aOperations research 606 $aManagement science 606 $aComputer simulation 606 $aArtificial Intelligence 606 $aComputational Intelligence 606 $aTheory of Computation 606 $aOperations Research, Management Science 606 $aComputer Modelling 606 $aOptimització combinatòria$2lemac 615 0$aArtificial intelligence. 615 0$aComputational intelligence. 615 0$aComputer science. 615 0$aOperations research. 615 0$aManagement science. 615 0$aComputer simulation. 615 14$aArtificial Intelligence. 615 24$aComputational Intelligence. 615 24$aTheory of Computation. 615 24$aOperations Research, Management Science. 615 24$aComputer Modelling. 615 7$aOptimització combinatòria 676 $a006.3 700 $aBlum$b C$g(Christian),$01648993 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910866571103321 996 $aConstruct, Merge, Solve & Adapt$94464661 997 $aUNINA