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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

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 proceedingConference contribution

Harvard

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

APA

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. https://doi.org/10.1007/978-3-030-12598-1_2

Vancouver

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). https://doi.org/10.1007/978-3-030-12598-1_2

Author

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).

Bibtex

@inproceedings{e3957a3131ac43cb93856f3cb8aaf4aa,
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",
author = "Miqing Li and Xin Yao",
year = "2019",
month = "2",
day = "3",
doi = "10.1007/978-3-030-12598-1_2",
language = "English",
isbn = "978-3-030-12597-4",
series = "Lecture Notes in Computer Science - Theoretical Computer Science and General Issues",
publisher = "Springer",
pages = "15--26",
editor = "Deb, {Kalyanmoy } and Goodman, {Erik } and {Coello Coello}, Carlos and Kathrin Klamroth and Kaisa Miettinen and Sanaz Mostaghim and Patrick Reed",
booktitle = "Evolutionary Multi-Criterion Optimization",

}

RIS

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

KW - archive

KW - optimality

KW - monotonicity

KW - empirical investigation

KW - evolutionary computation

U2 - 10.1007/978-3-030-12598-1_2

DO - 10.1007/978-3-030-12598-1_2

M3 - Conference contribution

SN - 978-3-030-12597-4

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

SP - 15

EP - 26

BT - Evolutionary Multi-Criterion Optimization

A2 - Deb, Kalyanmoy

A2 - Goodman, Erik

A2 - Coello Coello, Carlos

A2 - Klamroth, Kathrin

A2 - Miettinen, Kaisa

A2 - Mostaghim, Sanaz

A2 - Reed, Patrick

PB - Springer

ER -