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：在高维搜索空间中，在严格计算约束下追求最优权衡是多目标优化面临的根本挑战。我们提出NeuroPareto，一个融合了秩中心滤波、不确定性解耦与历史条件获取策略的统一架构，以导航复杂目标景观。校准贝叶斯分类器通过估计非支配层级间的认知不确定性，以最小评估成本快速生成高质量候选解。深度高斯过程代理进一步将预测不确定性分离为可约与不可约成分，为后续选择提供精细化的预测均值与风险感知信号。轻量级获取网络通过在线训练历史超体积改进，引导昂贵评估朝向平衡收敛性与多样性的区域。结合分层筛选与摊销代理更新，该方法在保持精度的同时维持低计算开销。在DTLZ与ZDT测试套件以及地下能源提取任务上的实验表明，NeuroPareto在帕累托接近度与超体积指标上持续优于增强分类器与代理辅助的基线方法。