An empirical investigation of the optimality and monotonicity properties of multiobjective archiving methods

Miqing Li; Xin Yao

doi:10.1007/978-3-030-12598-1_2

An empirical investigation of the optimality and monotonicity properties of multiobjective archiving methods

Miqing Li, Xin Yao

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

9 Citations (Scopus)

344 Downloads (Pure)

Abstract

Most evolutionary multiobjective optimisation (EMO) algorithms explicitly or implicitly maintain an archive for an approximation of the Pareto front. A question arising is whether existing archiving methods are reliable with respect to their convergence and approximation ability. Despite theoretical results available, it remains unknown how these archivers actually perform in practice. In particular, what percentage of solutions in their final archive are Pareto optimal? How frequently do they experience deterioration during the archiving process? Deterioration means archiving a new solution which is dominated by some solution discarded previously. This paper answers the above questions through a systematic investigation of eight representative archivers on 37 test instances with two to five objectives. We have found that 1) deterioration happens to all the archivers; 2) the deterioration degree can vary dramatically on different problems; 3) some archivers clearly perform better than others; and 4) several popular archivers sometime return a population with most solutions being the non-optimal. All of these suggest the need of improvement of current archiving methods.

Original language	English
Title of host publication	Evolutionary Multi-Criterion Optimization
Subtitle of host publication	10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings
Editors	Kalyanmoy Deb, Erik Goodman, Carlos Coello Coello, Kathrin Klamroth, Kaisa Miettinen, Sanaz Mostaghim, Patrick Reed
Publisher	Springer
Pages	15-26
Number of pages	12
ISBN (Electronic)	978-3-030-12598-1
ISBN (Print)	978-3-030-12597-4
DOIs	https://doi.org/10.1007/978-3-030-12598-1_2
Publication status	E-pub ahead of print - 3 Feb 2019
Event	10th International Conference on Evolutionary Multi-Criterion Optimization, (EMO 19) - East Lansing, Michigan, United States Duration: 10 Mar 2019 → 13 Mar 2019

Publication series

Name	Lecture Notes in Computer Science - Theoretical Computer Science and General Issues
Publisher	Springer
Volume	11411
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Conference on Evolutionary Multi-Criterion Optimization, (EMO 19)
Country/Territory	United States
City	East Lansing, Michigan
Period	10/03/19 → 13/03/19

Keywords

multi-objective optimisation
archive
optimality
monotonicity
empirical investigation
evolutionary computation

Access to Document

10.1007/978-3-030-12598-1_2Licence: None: All rights reserved

Miqing_Li_&_Xin_Yao_An_empirical_investigation_of_the_optimality_and_monotonicity_LNCS_2018
Checked for eligibility: 29/01/2019 This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer Science. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-12598-1_2
Accepted author manuscript, 3.47 MBLicence: None: All rights reserved

Cite this

Li, M., & Yao, X. (2019). An empirical investigation of the optimality and monotonicity properties of multiobjective archiving methods. In K. Deb, E. Goodman, C. Coello Coello, K. Klamroth, K. Miettinen, S. Mostaghim, & P. Reed (Eds.), Evolutionary Multi-Criterion Optimization: 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings (pp. 15-26). (Lecture Notes in Computer Science - Theoretical Computer Science and General Issues; Vol. 11411). Springer. Advance online publication. https://doi.org/10.1007/978-3-030-12598-1_2

Li, Miqing ; Yao, Xin. / An empirical investigation of the optimality and monotonicity properties of multiobjective archiving methods. Evolutionary Multi-Criterion Optimization: 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings. editor / Kalyanmoy Deb ; Erik Goodman ; Carlos Coello Coello ; Kathrin Klamroth ; Kaisa Miettinen ; Sanaz Mostaghim ; Patrick Reed. Springer, 2019. pp. 15-26 (Lecture Notes in Computer Science - Theoretical Computer Science and General Issues).

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title = "An empirical investigation of the optimality and monotonicity properties of multiobjective archiving methods",

abstract = "Most evolutionary multiobjective optimisation (EMO) algorithms explicitly or implicitly maintain an archive for an approximation of the Pareto front. A question arising is whether existing archiving methods are reliable with respect to their convergence and approximation ability. Despite theoretical results available, it remains unknown how these archivers actually perform in practice. In particular, what percentage of solutions in their final archive are Pareto optimal? How frequently do they experience deterioration during the archiving process? Deterioration means archiving a new solution which is dominated by some solution discarded previously. This paper answers the above questions through a systematic investigation of eight representative archivers on 37 test instances with two to five objectives. We have found that 1) deterioration happens to all the archivers; 2) the deterioration degree can vary dramatically on different problems; 3) some archivers clearly perform better than others; and 4) several popular archivers sometime return a population with most solutions being the non-optimal. All of these suggest the need of improvement of current archiving methods.",

keywords = "multi-objective optimisation, archive, optimality, monotonicity, empirical investigation, evolutionary computation",

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Li, M & Yao, X 2019, An empirical investigation of the optimality and monotonicity properties of multiobjective archiving methods. in K Deb, E Goodman, C Coello Coello, K Klamroth, K Miettinen, S Mostaghim & P Reed (eds), Evolutionary Multi-Criterion Optimization: 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings. Lecture Notes in Computer Science - Theoretical Computer Science and General Issues, vol. 11411, Springer, pp. 15-26, 10th International Conference on Evolutionary Multi-Criterion Optimization, (EMO 19), East Lansing, Michigan, United States, 10/03/19. https://doi.org/10.1007/978-3-030-12598-1_2

An empirical investigation of the optimality and monotonicity properties of multiobjective archiving methods. / Li, Miqing ; Yao, Xin.
Evolutionary Multi-Criterion Optimization: 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings. ed. / Kalyanmoy Deb; Erik Goodman; Carlos Coello Coello; Kathrin Klamroth; Kaisa Miettinen; Sanaz Mostaghim; Patrick Reed. Springer, 2019. p. 15-26 (Lecture Notes in Computer Science - Theoretical Computer Science and General Issues; Vol. 11411).

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

TY - GEN

T1 - An empirical investigation of the optimality and monotonicity properties of multiobjective archiving methods

AU - Li, Miqing

AU - Yao, Xin

PY - 2019/2/3

Y1 - 2019/2/3

N2 - Most evolutionary multiobjective optimisation (EMO) algorithms explicitly or implicitly maintain an archive for an approximation of the Pareto front. A question arising is whether existing archiving methods are reliable with respect to their convergence and approximation ability. Despite theoretical results available, it remains unknown how these archivers actually perform in practice. In particular, what percentage of solutions in their final archive are Pareto optimal? How frequently do they experience deterioration during the archiving process? Deterioration means archiving a new solution which is dominated by some solution discarded previously. This paper answers the above questions through a systematic investigation of eight representative archivers on 37 test instances with two to five objectives. We have found that 1) deterioration happens to all the archivers; 2) the deterioration degree can vary dramatically on different problems; 3) some archivers clearly perform better than others; and 4) several popular archivers sometime return a population with most solutions being the non-optimal. All of these suggest the need of improvement of current archiving methods.

AB - Most evolutionary multiobjective optimisation (EMO) algorithms explicitly or implicitly maintain an archive for an approximation of the Pareto front. A question arising is whether existing archiving methods are reliable with respect to their convergence and approximation ability. Despite theoretical results available, it remains unknown how these archivers actually perform in practice. In particular, what percentage of solutions in their final archive are Pareto optimal? How frequently do they experience deterioration during the archiving process? Deterioration means archiving a new solution which is dominated by some solution discarded previously. This paper answers the above questions through a systematic investigation of eight representative archivers on 37 test instances with two to five objectives. We have found that 1) deterioration happens to all the archivers; 2) the deterioration degree can vary dramatically on different problems; 3) some archivers clearly perform better than others; and 4) several popular archivers sometime return a population with most solutions being the non-optimal. All of these suggest the need of improvement of current archiving methods.

KW - multi-objective optimisation

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KW - optimality

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T3 - Lecture Notes in Computer Science - Theoretical Computer Science and General Issues

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BT - Evolutionary Multi-Criterion Optimization

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A2 - Coello Coello, Carlos

A2 - Klamroth, Kathrin

A2 - Miettinen, Kaisa

A2 - Mostaghim, Sanaz

A2 - Reed, Patrick

PB - Springer

T2 - 10th International Conference on Evolutionary Multi-Criterion Optimization, (EMO 19)

Y2 - 10 March 2019 through 13 March 2019

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Li M , Yao X. An empirical investigation of the optimality and monotonicity properties of multiobjective archiving methods. In Deb K, Goodman E, Coello Coello C, Klamroth K, Miettinen K, Mostaghim S, Reed P, editors, Evolutionary Multi-Criterion Optimization: 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings. Springer. 2019. p. 15-26. (Lecture Notes in Computer Science - Theoretical Computer Science and General Issues). Epub 2019 Feb 3. doi: 10.1007/978-3-030-12598-1_2

An empirical investigation of the optimality and monotonicity properties of multiobjective archiving methods

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