The pursuit of optimal trade-offs in high-dimensional search spaces under stringent computational constraints poses a fundamental challenge for contemporary multi-objective optimization. We develop NeuroPareto, a cohesive architecture that integrates rank-centric filtering, uncertainty disentanglement, and history-conditioned acquisition strategies to navigate complex objective landscapes. A calibrated Bayesian classifier estimates epistemic uncertainty across non-domination tiers, enabling rapid generation of high-quality candidates with minimal evaluation cost. Deep Gaussian Process surrogates further separate predictive uncertainty into reducible and irreducible components, providing refined predictive means and risk-aware signals for downstream selection. A lightweight acquisition network, trained online from historical hypervolume improvements, guides expensive evaluations toward regions balancing convergence and diversity. With hierarchical screening and amortized surrogate updates, the method maintains accuracy while keeping computational overhead low. Experiments on DTLZ and ZDT suites and a subsurface energy extraction task show that NeuroPareto consistently outperforms classifier-enhanced and surrogate-assisted baselines in Pareto proximity and hypervolume.

翻译：在严苛计算约束下，于高维搜索空间中寻求最优权衡，是当代多目标优化面临的一项根本性挑战。本文提出NeuroPareto，一种集成秩中心过滤、不确定性解耦与历史条件采集策略的协同架构，用于探索复杂的目标空间。一个经过校准的贝叶斯分类器估计跨非支配层级的知识不确定性，从而能够以最低评估成本快速生成高质量候选解。深度高斯过程代理模型进一步将预测不确定性分解为可约与不可约分量，为下游选择提供精细化的预测均值与风险感知信号。一个从历史超体积改进中在线训练的轻量级采集网络，引导昂贵评估朝向兼顾收敛性与多样性的区域。通过分层筛选与摊销式代理模型更新，该方法在保持精度的同时维持较低计算开销。在DTLZ与ZDT测试集以及一项地下能源开采任务上的实验表明，NeuroPareto在帕累托逼近度与超体积指标上持续优于分类器增强与代理模型辅助的基线方法。