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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



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.

Start Date Sep 5, 2020
Publisher Springer (part of Springer Nature)
Series Title Lecture notes in computer science
Series ISSN 0302-9743
Institution Citation ZĂVOIANU, A.-C., LACROIX, B. and MCCALL, J. 2020. Comparative run-time performance of evolutionary algorithms on multi-objective interpolated continuous optimisation problems. To be presented at 16th International Parallel problem solving from nature conference (PPSN 2020), 5-9 September 2020, Leiden, Netherlands. Lecture notes in computer science (LNCS). Cham; Springer, (accepted).
Keywords Multi-objective continuous optimisation; Evolutionary algorithms; Performance analysis; Large-scale benchmarking

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