---
title: "Inner optimizers in active-learning Kriging reliability analysis: a pre-specified comparison and the unconservative estimates left by unlearned failure modes"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.03117v1"
permalink: "https://feed.astralor.com/items/itm_998a8b1beae0c2b63f95695b"
published: "2026-11-02T13:34:09.000Z"
score: 59
channel: "ai"
tags: ["RAG","Inner","Kriging","Active-learning Kriging","L-BFGS-B","Holm-corrected","With"]
---

# Inner optimizers in active-learning Kriging reliability analysis: a pre-specified comparison and the unconservative estimates left by unlearned failure modes

## 速览

Active-learning Kriging often replaces candidate pools by continuous metaheuristic search. A pre-specified comparison sharing surrogate, population and stopping rule isolated this inner optimizer: five budget-matched co…

## 关键点

- Active-learning Kriging often replaces candidate pools by continuous metaheuristic search.
- A pre-specified comparison sharing surrogate, population and stopping rule isolated this inner optimizer: five budget-matched continuous optimizers, including hybrid swarm optimization with L-BFGS-B polishing, against c…
- The primary hypothesis was not supported: against optimizers sharing its criterion the hybrid won 6, tied 41 and lost 19 of 66 Holm-corrected accuracy comparisons, and against pool methods 0, 23 and 21 of 44.

## 全文

Active-learning Kriging often replaces candidate pools by continuous metaheuristic search. A pre-specified comparison sharing surrogate, population and stopping rule isolated this inner optimizer: five budget-matched continuous optimizers, including hybrid swarm optimization with L-BFGS-B polishing, against candidate pools on eleven problems (dimension 2 to 100). The primary hypothesis was not supported: against optimizers sharing its criterion the hybrid won 6, tied 41 and lost 19 of 66 Holm-corrected accuracy comparisons, and against pool methods 0, 23 and 21 of 44. With sequential selection on three series systems, multistart L-BFGS-B left a failure mode unlearned in 59 of 60 runs, more often than swarm, genetic-algorithm or AK-MCS. In all 198 runs (22 post-freeze) with an unlearned mode, the learned modes did not compensate and failure probability was underestimated (exploratory). The error criterion can accept substantial underestimation: it accepted 17 runs (13 pool-selected) at 0.38 to 0.89 of the reference; 181 exhausted the budget. Follow-up analyses on single-design-point problems indicate underestimation when selections concentrate near the design point. Kriging-believer pool selection satisfied the surrogate stopping criterion with fewer calls than the best continuous method in all four informative batch cells (two post-freeze). We recommend default pool selection and coverage checks for any selection.

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