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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Table of contents (10 chapters)
-
Basic Concepts
-
Theory and Algorithms
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
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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