arbiter
JournalsPricingAccount

An ensemble learning-enhanced collaborative surrogate modeling approach with improved particle swarm optimization for structural reliability assessment

Hongmin Li et al.

Computers in Industry2026https://doi.org/10.1016/j.compind.2026.104441article
AJG 3
Weight
0.37

What the paper says

No abstract available.

1 citation

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1016/j.compind.2026.104441

Or copy a formatted citation

@article{hongmin2026,
  title        = {{An ensemble learning-enhanced collaborative surrogate modeling approach with improved particle swarm optimization for structural reliability assessment}},
  author       = {Hongmin Li et al.},
  journal      = {Computers in Industry},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.compind.2026.104441},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

An ensemble learning-enhanced collaborative surrogate modeling approach with improved particle swarm optimization for structural reliability assessment

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.