Overview
- Summarizes non-convex multi-objective optimization problems and methods
- Supplies comprehensive coverage, theoretical background, and examples of practical applications
- Explains several directions of multi-objective optimization research
- Includes supplementary material: sn.pub/extras
Part of the book series: Springer Optimization and Its Applications (SOIA, volume 123)
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About this book
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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Keywords
- Branch-and-Bound approach
- Lipschitz optimization
- applications in engineering
- non-convex multi-objective optimization
- randomized algorithms
- software and applications
- Scalarization
- Tchebycheff Method
- Pareto Sets
- Normal Boundary Intersection
- Statistical Models for Global Optimization
- Optimal Algorithms for Lipschitz Functions
- Optimal Passive Algorithm
- Optimal Sequential Algorithm
- Multidimensional Bi-Objective Lipschitz Optimization
- Pareto Frontier
- Trisection of a Hyper-rectangle
- Pareto Optimal Decisions
- Binary-Linear Model
- continuous problems
Table of contents (10 chapters)
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Front Matter
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Basic Concepts
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Front Matter
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Theory and Algorithms
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Front Matter
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Back Matter
Reviews
“Readers will definitely enjoy this book, because all surveyed topics are rigorously exposed. Moreover, since the main prerequisites are provided, the book is essentially self-contained and easy to read. The authors have also included many illustrative pictures that ensure a good understanding of technical concepts and results. … this book is an excellent reference for researchers and graduate students in both pure and applied mathematics, as well as other disciplines.” (Nicolae Popovici, Mathematical Reviews, August, 2018)
Authors and Affiliations
Bibliographic Information
Book Title: Non-Convex Multi-Objective Optimization
Authors: Panos M. Pardalos, Antanas Žilinskas, Julius Žilinskas
Series Title: Springer Optimization and Its Applications
DOI: https://doi.org/10.1007/978-3-319-61007-8
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-61005-4Published: 09 August 2017
Softcover ISBN: 978-3-319-86981-0Published: 15 June 2018
Series ISSN: 1931-6828
Series E-ISSN: 1931-6836
Edition Number: 1
Number of Pages: XII, 192
Number of Illustrations: 14 b/w illustrations, 4 illustrations in colour
Topics: Optimization, Algorithms, Mathematical Applications in Computer Science