research-article

Metric for evaluating normalization methods in multiobjective optimization

Authors:

Hisao Ishibuchi,

Dipti SrinivasanAuthors Info & Claims

GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference

Pages 403 - 411

https://doi.org/10.1145/3449639.3459388

Published: 26 June 2021 Publication History

Abstract

Normalization is an important algorithmic component for multiobjective evolutionary algorithms (MOEAs). Different normalization methods have been proposed in the literature. Recently, several studies have been conducted to examine the effects of normalization methods. However, the existing evaluation methods for investigating the effects of normalization are limited due to their drawbacks. In this paper, we discuss the limitations of the existing evaluation methods. A new metric has been proposed to facilitate the investigation of normalization methods. Our analysis clearly shows the superiority of the proposed metric over the existing methods. We also use the proposed metric to compare three popular normalization methods on problems with different Pareto front shapes.

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Cited By

Tanabe R(2024)Investigating normalization in preference-based evolutionary multi-objective optimization using a reference pointApplied Soft Computing10.1016/j.asoc.2024.111646159:COnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111646
Efatmaneshnik MChitsaz NQiao L(2023)Decision making for multi‐objective problems: Mean and median metricsSystems Engineering10.1002/sys.2169026:6(814-829)Online publication date: 12-May-2023
https://doi.org/10.1002/sys.21690
He LShang KNan YIshibuchi HSrinivasan D(2022)Relation Between Objective Space Normalization and Weight Vector Scaling in Decomposition-Based Multiobjective Evolutionary AlgorithmsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.319210027:5(1177-1191)Online publication date: 18-Jul-2022
https://dl.acm.org/doi/10.1109/TEVC.2022.3192100

Index Terms

Metric for evaluating normalization methods in multiobjective optimization
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
      1. Discrete optimization
        Optimization with randomized search heuristics
        Evolutionary algorithms

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cover image ACM Conferences

GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference

June 2021

1219 pages

ISBN:9781450383509

DOI:10.1145/3449639

Editor:
Francisco Chicano
University of Malaga
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General Chair:
Krzysztof Krawiec
Poznan University of Technology

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Published: 26 June 2021

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the National Research Foundation Singapore under its AI Singapore Programme
the Program for Guangdong Introducing Innovative and Enterpreneurial Teams
Guangdong Provincial Key Laboratory
the Program for University Key Laboratory of Guangdong Province
Shenzhen Science and Technology Program
National Natural Science Foundation of China

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GECCO '21

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SIGEVO

GECCO '21: Genetic and Evolutionary Computation Conference

July 10 - 14, 2021

Lille, France

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Cited By

Tanabe R(2024)Investigating normalization in preference-based evolutionary multi-objective optimization using a reference pointApplied Soft Computing10.1016/j.asoc.2024.111646159:COnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111646
Efatmaneshnik MChitsaz NQiao L(2023)Decision making for multi‐objective problems: Mean and median metricsSystems Engineering10.1002/sys.2169026:6(814-829)Online publication date: 12-May-2023
https://doi.org/10.1002/sys.21690
He LShang KNan YIshibuchi HSrinivasan D(2022)Relation Between Objective Space Normalization and Weight Vector Scaling in Decomposition-Based Multiobjective Evolutionary AlgorithmsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.319210027:5(1177-1191)Online publication date: 18-Jul-2022
https://dl.acm.org/doi/10.1109/TEVC.2022.3192100

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