Title: U-RankMOEA: Learning to Optimize High-Dimensional Expensive Multi-Objective Problems
Primary area: optimization
Keywords: surrogate-assisted evolutionary algorithms, multi-objective optimization, deep Gaussian processes, uncertainty quantification, learned acquisition functions, high-dimensional optimization

Abstract: We propose U-RankMOEA, a modular and uncertainty-aware framework for high-dimensional, expensive multi-objective optimization under tight evaluation budgets. The framework integrates a Bayesian classifier with calibrated epistemic uncertainty for rank-guided offspring generation, Deep Gaussian Process surrogates to disentangle epistemic and aleatoric uncertainty for informed search, and a history-aware acquisition network that adaptively prioritizes evaluations based on past hypervolume gains.
To reduce cost while maintaining fidelity, U-RankMOEA incorporates complexity-aware strategies including two-stage screening, adaptive uncertainty sampling, and amortized surrogate updates. Empirical results on DTLZ and ZDT benchmarks, as well as a geothermal reservoir optimization task, show that U-RankMOEA significantly improves IGD and hypervolume using only a few hundred true evaluations.
Our method demonstrates the synergy of calibrated uncertainty, expressive surrogates, and learned acquisition for scalable, representation-driven optimization in domains where evaluations are costly and decision spaces are large.

TL;DR: U-RankMOEA combines Bayesian classification, Deep Gaussian Process surrogates, and learned acquisition to efficiently solve high-dimensional multi-objective optimization under tight evaluation budgets.