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ResearchFormerly WizWand

Offline Multi-Objective Optimization on Off-MOO-Bench Sci-Design

1.8Average IGDoffline RankD(best)
1.40724.05866.719.3614Jun 13, 2026
Updated 4mo ago

Evaluation results

MethodLinks
D(best)Number of solutions=256, Percentile=50th2026.061.8
ParetoFlow2026.063.21
MOBO2026.064.95
Multiple ModelsExtension=COMs2026.066.1
Multi Head + GradNormNumber of solutions=256, Percentile=50th2026.066.2
ParetoFlowNumber of solutions=256, Percentile=50th2026.066.21
Multi HeadOptimization=PcGrad2026.066.25
Multiple ModelsExtension=IOM2026.066.45
MultipleModels + COMsNumber of solutions=256, Percentile=50th2026.066.72
End-to-EndOptimization=PcGrad2026.066.97
Multi HeadNumber of solutions=256, Percentile=50th2026.067.17
Multiple ModelsExtension=ICT2026.067.4
End-to-End + PcGradNumber of solutions=256, Percentile=50th2026.067.47
DOMOO2026.067.55
Multiple ModelsExtension=RoMA2026.067.97
MOBO-qParEGONumber of solutions=256, Percentile=50th2026.068
Multiple Models + RoMANumber of solutions=256, Percentile=50th2026.068.15
MOBOAcquisition Function=qParEGO2026.068.3
Multi Head + PcGradNumber of solutions=256, Percentile=50th2026.068.35
DOMOONumber of solutions=256, Percentile=50th2026.068.6
End-to-End2026.068.68
Multi HeadOptimization=GradNorm2026.068.95
End-to-EndNumber of solutions=256, Percentile=50th2026.068.97
End-to-EndOptimization=GradNorm2026.069.25
Multi Head2026.069.25
Multiple Models + ICTNumber of solutions=256, Percentile=50th2026.069.55
End-to-End + GradNormNumber of solutions=256, Percentile=50th2026.069.57
Multiple ModelsNumber of solutions=256, Percentile=50th2026.069.57
D(best)2026.069.62
Multiple Models + IOMNumber of solutions=256, Percentile=50th2026.069.78
MOBONumber of solutions=256, Percentile=50th2026.0610.27
Multiple Models + Tri-MentoringNumber of solutions=256, Percentile=50th2026.0610.3
Multiple Models2026.0611.03
Multiple ModelsExtension=Tri-Mentoring2026.0611.62