Angle-Based Crowding Degree Estimation for Many-Objective Optimization

Yani Xue; Miqing Li; Xiaohui Liu

doi:10.1007/978-3-030-44584-3_45

Angle-Based Crowding Degree Estimation for Many-Objective Optimization

Yani Xue^*, Miqing Li, Xiaohui Liu

^*Corresponding author for this work

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

Many-objective optimization, which deals with an optimization problem with more than three objectives, poses a big challenge to various search techniques, including evolutionary algorithms. Recently, a meta-objective optimization approach (called bi-goal evolution, BiGE) which maps solutions from the original high-dimensional objective space into a bi-goal space of proximity and crowding degree has received increasing attention in the area. However, it has been found that BiGE tends to struggle on a class of many-objective problems where the search process involves dominance resistant solutions, namely, those solutions with an extremely poor value in at least one of the objectives but with (near) optimal values in some of the others. It is difficult for BiGE to get rid of dominance resistant solutions as they are Pareto nondominated and far away from the main population, thus always having a good crowding degree. In this paper, we propose an angle-based crowding degree estimation method for BiGE (denoted as aBiGE) to replace distance-based crowding degree estimation in BiGE. Experimental studies show the effectiveness of this replacement.

Original language	English
Title of host publication	Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Proceedings
Editors	Michael R. Berthold, Ad Feelders, Georg Krempl
Publisher	Springer Vieweg
Pages	574-586
Number of pages	13
ISBN (Print)	9783030445836
DOIs	https://doi.org/10.1007/978-3-030-44584-3_45
Publication status	Published - 2020
Event	18th International Conference on Intelligent Data Analysis, IDA 2020 - Konstanz, Germany Duration: 27 Apr 2020 → 29 Apr 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12080 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th International Conference on Intelligent Data Analysis, IDA 2020
Country/Territory	Germany
City	Konstanz
Period	27/04/20 → 29/04/20

Bibliographical note

Publisher Copyright:
© 2020, The Author(s).

Keywords

Angle-based crowding degree estimation
Bi-goal evolution
Evolutionary algorithm
Many-objective optimization

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-44584-3_45

Cite this

Xue, Y., Li, M., & Liu, X. (2020). Angle-Based Crowding Degree Estimation for Many-Objective Optimization. In M. R. Berthold, A. Feelders, & G. Krempl (Eds.), Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Proceedings (pp. 574-586). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12080 LNCS). Springer Vieweg. https://doi.org/10.1007/978-3-030-44584-3_45

Xue, Yani ; Li, Miqing ; Liu, Xiaohui. / Angle-Based Crowding Degree Estimation for Many-Objective Optimization. Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Proceedings. editor / Michael R. Berthold ; Ad Feelders ; Georg Krempl. Springer Vieweg, 2020. pp. 574-586 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Angle-Based Crowding Degree Estimation for Many-Objective Optimization",

abstract = "Many-objective optimization, which deals with an optimization problem with more than three objectives, poses a big challenge to various search techniques, including evolutionary algorithms. Recently, a meta-objective optimization approach (called bi-goal evolution, BiGE) which maps solutions from the original high-dimensional objective space into a bi-goal space of proximity and crowding degree has received increasing attention in the area. However, it has been found that BiGE tends to struggle on a class of many-objective problems where the search process involves dominance resistant solutions, namely, those solutions with an extremely poor value in at least one of the objectives but with (near) optimal values in some of the others. It is difficult for BiGE to get rid of dominance resistant solutions as they are Pareto nondominated and far away from the main population, thus always having a good crowding degree. In this paper, we propose an angle-based crowding degree estimation method for BiGE (denoted as aBiGE) to replace distance-based crowding degree estimation in BiGE. Experimental studies show the effectiveness of this replacement.",

keywords = "Angle-based crowding degree estimation, Bi-goal evolution, Evolutionary algorithm, Many-objective optimization",

author = "Yani Xue and Miqing Li and Xiaohui Liu",

note = "Publisher Copyright: {\textcopyright} 2020, The Author(s).; 18th International Conference on Intelligent Data Analysis, IDA 2020 ; Conference date: 27-04-2020 Through 29-04-2020",

year = "2020",

doi = "10.1007/978-3-030-44584-3_45",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Xue, Y, Li, M & Liu, X 2020, Angle-Based Crowding Degree Estimation for Many-Objective Optimization. in MR Berthold, A Feelders & G Krempl (eds), Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12080 LNCS, Springer Vieweg, pp. 574-586, 18th International Conference on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, 27/04/20. https://doi.org/10.1007/978-3-030-44584-3_45

Angle-Based Crowding Degree Estimation for Many-Objective Optimization. / Xue, Yani; Li, Miqing; Liu, Xiaohui.
Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Proceedings. ed. / Michael R. Berthold; Ad Feelders; Georg Krempl. Springer Vieweg, 2020. p. 574-586 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12080 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Angle-Based Crowding Degree Estimation for Many-Objective Optimization

AU - Xue, Yani

AU - Li, Miqing

AU - Liu, Xiaohui

PY - 2020

Y1 - 2020

N2 - Many-objective optimization, which deals with an optimization problem with more than three objectives, poses a big challenge to various search techniques, including evolutionary algorithms. Recently, a meta-objective optimization approach (called bi-goal evolution, BiGE) which maps solutions from the original high-dimensional objective space into a bi-goal space of proximity and crowding degree has received increasing attention in the area. However, it has been found that BiGE tends to struggle on a class of many-objective problems where the search process involves dominance resistant solutions, namely, those solutions with an extremely poor value in at least one of the objectives but with (near) optimal values in some of the others. It is difficult for BiGE to get rid of dominance resistant solutions as they are Pareto nondominated and far away from the main population, thus always having a good crowding degree. In this paper, we propose an angle-based crowding degree estimation method for BiGE (denoted as aBiGE) to replace distance-based crowding degree estimation in BiGE. Experimental studies show the effectiveness of this replacement.

AB - Many-objective optimization, which deals with an optimization problem with more than three objectives, poses a big challenge to various search techniques, including evolutionary algorithms. Recently, a meta-objective optimization approach (called bi-goal evolution, BiGE) which maps solutions from the original high-dimensional objective space into a bi-goal space of proximity and crowding degree has received increasing attention in the area. However, it has been found that BiGE tends to struggle on a class of many-objective problems where the search process involves dominance resistant solutions, namely, those solutions with an extremely poor value in at least one of the objectives but with (near) optimal values in some of the others. It is difficult for BiGE to get rid of dominance resistant solutions as they are Pareto nondominated and far away from the main population, thus always having a good crowding degree. In this paper, we propose an angle-based crowding degree estimation method for BiGE (denoted as aBiGE) to replace distance-based crowding degree estimation in BiGE. Experimental studies show the effectiveness of this replacement.

KW - Angle-based crowding degree estimation

KW - Bi-goal evolution

KW - Evolutionary algorithm

KW - Many-objective optimization

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DO - 10.1007/978-3-030-44584-3_45

M3 - Conference contribution

AN - SCOPUS:85084249661

SN - 9783030445836

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 574

EP - 586

BT - Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Proceedings

A2 - Berthold, Michael R.

A2 - Feelders, Ad

A2 - Krempl, Georg

PB - Springer Vieweg

T2 - 18th International Conference on Intelligent Data Analysis, IDA 2020

Y2 - 27 April 2020 through 29 April 2020

ER -

Xue Y, Li M, Liu X. Angle-Based Crowding Degree Estimation for Many-Objective Optimization. In Berthold MR, Feelders A, Krempl G, editors, Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Proceedings. Springer Vieweg. 2020. p. 574-586. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-44584-3_45

Angle-Based Crowding Degree Estimation for Many-Objective Optimization

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this