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On the Effect of Scalarising Norm Choice in a ParEGO implementation Read chapter Read first chapter

2017 | OriginalPaper | Chapter

A Comparative Study of Fast Adaptive Preference-Guided Evolutionary Multi-objective Optimization

Authors : Florian Siegmund, Amos H. C. Ng, Kalyanmoy Deb

Published in: Evolutionary Multi-Criterion Optimization

Publisher: Springer International Publishing

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Abstract

In Simulation-based Evolutionary Multi-objective Optimization, the number of simulation runs is very limited, since the complex simulation models require long execution times. With the help of preference information, the optimization result can be improved by guiding the optimization towards relevant areas in the objective space with, for example, the Reference Point-based NSGA-II algorithm (R-NSGA-II) [4]. Since the Pareto-relation is the primary fitness function in R-NSGA-II, the algorithm focuses on exploring the objective space with high diversity. Only after the population has converged close to the Pareto-front does the influence of the reference point distance as secondary fitness criterion increase and the algorithm converges towards the preferred area on the Pareto-front.
In this paper, we propose a set of extensions of R-NSGA-II which adaptively control the algorithm behavior, in order to converge faster towards the reference point. The adaption can be based on criteria such as elapsed optimization time or the reference point distance, or a combination thereof. In order to evaluate the performance of the adaptive extensions of R-NSGA-II, a performance metric for reference point-based EMO algorithms is used, which is based on the Hypervolume measure called the Focused Hypervolume metric [12]. It measures convergence and diversity of the population in the preferred area around the reference point. The results are evaluated on two benchmark problems of different complexity and a simplistic production line model.

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previous chapter Towards a Better Balance of Diversity and Convergence in NSGA-III: First Results
next chapter A Population-Based Algorithm for Learning a Majority Rule Sorting Model with Coalitional Veto
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Metadata
Title
A Comparative Study of Fast Adaptive Preference-Guided Evolutionary Multi-objective Optimization
Authors
Florian Siegmund
Amos H. C. Ng
Kalyanmoy Deb
Copyright Year
2017
Book
Evolutionary Multi-Criterion Optimization

Print ISBN: 978-3-319-54156-3

Electronic ISBN: 978-3-319-54157-0

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-54157-0_38

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