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Non-convex multi-objective optimization [[electronic resource] /] / by Panos M. Pardalos, Antanas Žilinskas, Julius Žilinskas



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Autore: Pardalos Panos M Visualizza persona
Titolo: Non-convex multi-objective optimization [[electronic resource] /] / by Panos M. Pardalos, Antanas Žilinskas, Julius Žilinskas Visualizza cluster
Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017
Edizione: 1st ed. 2017.
Descrizione fisica: 1 online resource (192 pages) : illustrations, tables
Disciplina: 519.3
Soggetto topico: Mathematical optimization
Algorithms
Computer science—Mathematics
Computer mathematics
Optimization
Mathematical Applications in Computer Science
Persona (resp. second.): ŽilinskasAntanas
ŽilinskasJulius
Nota di bibliografia: Includes bibliographical references and index.
Nota di contenuto: 1. Definitions and Examples -- 2. Scalarization -- 3. Approximation and Complexity -- 4. A Brief Review of Non-Convex Single-Objective Optimization -- 5. Multi-Objective Branch and Bound -- 6. Worst-Case Optimal Algorithms -- 7. Statistical Models Based Algorithms -- 8. Probabilistic Bounds in Multi-Objective Optimization -- 9. Visualization of a Set of Pareto Optimal Decisions -- 10. Multi-Objective Optimization Aided Visualization of Business Process Diagrams. –References -- Index.
Sommario/riassunto: Recent results on non-convex multi-objective optimization problems and methods are presented in this book, with particular attention to expensive black-box objective functions. Multi-objective optimization methods facilitate designers, engineers, and researchers to make decisions on appropriate trade-offs between various conflicting goals. A variety of deterministic and stochastic multi-objective optimization methods are developed in this book. Beginning with basic concepts and a review of non-convex single-objective optimization problems; this book moves on to cover multi-objective branch and bound algorithms, worst-case optimal algorithms (for Lipschitz functions and bi-objective problems), statistical models based algorithms, and probabilistic branch and bound approach. Detailed descriptions of new algorithms for non-convex multi-objective optimization, their theoretical substantiation, and examples for practical applications to the cell formation problem in manufacturing engineering, the process design in chemical engineering, and business process management are included to aide researchers and graduate students in mathematics, computer science, engineering, economics, and business management. .
Titolo autorizzato: Non-convex multi-objective optimization  Visualizza cluster
ISBN: 3-319-61007-4
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910799219203321
Lo trovi qui: Univ. Federico II
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Serie: Springer Optimization and Its Applications, . 1931-6828 ; ; 123