Experimental Results for the study "The Hypervolume Newton Method for Constrained Multi-Objective Optimization Problems"
Zenodo (CERN European Organization for Nuclear Research)
Abstract
This repository contains the experiment results (raw data in NPZ and CSV format and Latex tables) for the study "The Hypervolume Newton Method for Constrained Multi-Objective Optimization Problems", which is accepted in Mathematical and Computational Applications journal. The preprint version of the related paper is already online: Wang, H.; Emmerich, M.; Deutz, A.; Hernández, V.A.S.; Schütze, O. The Hypervolume Newton Method for Constrained Multi-objective Optimization Problems. Preprints 2022, 2022110103 (doi: 10.20944/preprints202211.0103.v1). Data description: we benchmarked three algorithms: (1) the standalone Hypervolume Netwon Method (HVN), (2) NSGA-III, and (3) the hybridization of the standalone HVN and NSGA-III on several artificial problems. For the standalone HVN algorithm, we tested it on three simple artificial test problems - P1, P2, and P3 (proposed in the above paper): 2D-example-50*.tex: problem P1 3D-example1*.tex: problem P2 3D-example2*.tex: problem P3 For NSGA-III and the hybridization, we tested them on the equality-constrained DTLZ and Inverted DTLZ (IDTLZ) problems: Eq1DTLZ.*npz: DTLZ problems Eq1IDTLZ*.npz: IDTLZ problems
Authors 5
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Hao Wang Aachen
Affiliation as printed
Leiden University
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Michael Emmerich Aachen
Affiliation as printed
Leiden University
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Andre H. Deutz Aachen
Affiliation as printed
Leiden University
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Affiliation as printed
Tecnologico de Monterrey, School of Engineering and Sciences
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Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional
Affiliation as printed
Computer Science Department, Cinvestav-IPN
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