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Comparative run-time performance of evolutionary algorithms on multi-objective interpolated continuous optimisation problems.

Zăvoianu, Alexandru-Ciprian; Lacroix, Benjamin; McCall, John

Authors

John McCall



Contributors

Thomas Bäck
Editor

Mike Preuss
Editor

André Deutz
Editor

Hao Wang
Editor

Carola Doerr
Editor

Michael Emmerich
Editor

Heike Trautmann
Editor

Abstract

We propose a new class of multi-objective benchmark problems on which we analyse the performance of four well established multi-objective evolutionary algorithms (MOEAs) – each implementing a different search paradigm – by comparing run-time convergence behaviour over a set of 1200 problem instances. The new benchmarks are created by fusing previously proposed single-objective interpolated continuous optimisation problems (ICOPs) via a common set of Pareto non-dominated seeds. They thus inherit the ICOP property of having tunable fitness landscape features. The benchmarks are of intrinsic interest as they derive from interpolation methods and so can approximate general problem instances. This property is revealed to be of particular importance as our extensive set of numerical experiments indicates that choices pertaining to (i) the weighting of the inverse distance interpolation function and (ii) the problem dimension can be used to construct problems that are challenging to all tested multi-objective search paradigms. This in turn means that the new multi-objective ICOPs problems (MO-ICOPs) can be used to construct well-balanced benchmark sets that discriminate well between the run-time convergence behaviour of different solvers.

Citation

ZĂVOIANU, A.-C., LACROIX, B. and MCCALL, J. 2020. Comparative run-time performance of evolutionary algorithms on multi-objective interpolated continuous optimisation problems. In Bäck, T., Preuss, M., Deutz, A., Wang, H., Doerr, C., Emmerich, M. and Trautmann, H. (eds.) Parallel problem solving from nature: PPSN XVI: proceedings of the 16th Parallel problem solving from nature international conference (PPSN 2020), 5-9 September 2020, Leiden, The Netherlands. Lecture notes in computer science, 12269. Cham; Springer, part 1, pages 287-300. Available from: https://doi.org/10.1007/978-3-030-58112-1_20

Conference Name 16th Parallel problem solving from nature international conference 2020 (PPSN 2020)
Conference Location Leiden, The Netherlands
Start Date Sep 5, 2020
End Date Sep 9, 2020
Acceptance Date May 28, 2020
Online Publication Date Sep 22, 2020
Publication Date Dec 31, 2020
Deposit Date Jul 6, 2020
Publicly Available Date Jul 6, 2020
Publisher Springer Verlag
Volume 12269
Pages 287-300
Series Title Lecture notes on computer science
Series Number 12269
Series ISSN 0302-9743
Book Title Parallel problem solving from nature: PPSN XVI: proceedings of the 16th Parallel problem solving from nature international conference (PPSN 2020), 5-9 September 2020, Leiden, The Netherlands.
ISBN 9783030581114
DOI https://doi.org/10.1007/978-3-030-58112-1_20
Keywords Multi-objective continuous optimisation; Evolutionary algorithms; Performance analysis; Large-scale benchmarking
Public URL https://rgu-repository.worktribe.com/output/943801

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