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Non-Convex Multi-Objective Optimization: 123 (Springer Optimization and Its Applications, 123) - Hardcover

Pardalos, Panos M.; Žilinskas, Antanas; Žilinskas, Julius

 
9783319610054: Non-Convex Multi-Objective Optimization: 123 (Springer Optimization and Its Applications, 123)
Hardcover
ISBN 10: 3319610058 ISBN 13: 9783319610054
Publisher: Springer, 2017

Synopsis

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.  

"synopsis" may belong to another edition of this title.

From the Back Cover

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. 

"About this title" may belong to another edition of this title.

Publisher
Springer
Publication date
2017
Language
English
ISBN 10 
3319610058
ISBN 13 
9783319610054
Binding
Hardcover
Edition number
1
Number of pages
204