@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 = feb,
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",
note = "10th International Conference on Evolutionary Multi-Criterion Optimization, (EMO 19) ; Conference date: 10-03-2019 Through 13-03-2019",
}