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Erschienen in:
A Survey of Diversity Oriented Optimization: Problems, Indicators, and Algorithms Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Global Multi-objective Optimization by Means of Cell Mapping Techniques

verfasst von : Carlos Hernández, Oliver Schütze, Jian-Qiao Sun

Erschienen in: EVOLVE – A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation VII

Verlag: Springer International Publishing

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Abstract

Multi-objective optimization problems (MOPs) arise in many fields in engineering. In this chapter we argue that adaptation of cell mapping techniques, originally designed for the global analysis of dynamical systems, are well-suited for the thorough analysis of low-dimensional MOPs. Algorithms of this kind deliver an approximation of the set of global solutions, the Pareto set, as well as the set of locally optimal and nearly optimal solutions in one run of the algorithm which may significantly improve the underlying decision making process. We underline the statements on some illustrative examples and present comparisons to other algorithms.

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Vorheriges Kapitel A Survey of Diversity Oriented Optimization: Problems, Indicators, and Algorithms
Nächstes Kapitel Percentile via Polynomial Chaos Expansion: Bridging Robust Optimization with Reliability
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Metadaten
Titel
Global Multi-objective Optimization by Means of Cell Mapping Techniques
verfasst von
Carlos Hernández
Oliver Schütze
Jian-Qiao Sun
Copyright-Jahr
2017
Buch
EVOLVE – A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation VII

Print ISBN: 978-3-319-49324-4

Electronic ISBN: 978-3-319-49325-1

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-49325-1_2

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