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Non-convex multi-objective optimization

Pardalos, Panos M.

Non-convex multi-objective optimization

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Non-convex multi-objective optimizationby Panos M. Pardalos, Antanas Zilinskas, Julius Zilinskas.
作者:	Pardalos, Panos M.
其他作者:	Zilinskas, Antanas.
出版者:	Cham :Springer International Publishing :2017.
面頁冊數:	xii, 192 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Mathematical optimization.
電子資源:	http://dx.doi.org/10.1007/978-3-319-61007-8
ISBN:	9783319610078$q(electronic bk.)

Non-convex multi-objective optimization
Pardalos, Panos M.

Non-convex multi-objective optimization[electronic resource] /by Panos M. Pardalos, Antanas Zilinskas, Julius Zilinskas. - Cham :Springer International Publishing :2017. - xii, 192 p. :ill., digital ;24 cm. - Springer optimization and its applications,v.1231931-6828 ;. - Springer optimization and its applications ;v. 3..

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.

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.

ISBN: 9783319610078$q(electronic bk.)

Standard No.: 10.1007/978-3-319-61007-8doiSubjects--Topical Terms:

183292
Mathematical optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

Non-convex multi-objective optimization
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520 $a 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.
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