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pith:IFBIJ64D

pith:2026:IFBIJ64DKZVZ6JPG6SZADS76NL

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TRUST-TAEA: A trustworthiness-guided two-archive evolutionary algorithm with variable-grouping sparse search for large-scale multi-objective optimization

JunYi Cui

TRUST-TAEA defines trustworthiness from evolutionary progress and archive maturity to coordinate variable-grouping sparse search in large-scale multi-objective optimization.

arxiv:2605.13324 v1 · 2026-05-13 · math.OC · cs.NE

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Claims

C1strongest claim

TRUST-TAEA achieves superior or highly competitive performance in terms of convergence, diversity, and stability on the LSMOP benchmark suite with 500--5000 decision variables and obtains the best IGD+ value on a three-objective microgrid scheduling case.

C2weakest assumption

The assumption that integrating evolutionary progress with convergence-archive maturity produces a reliable trustworthiness signal that can safely coordinate variable-grouping sparse search and archive stabilization without introducing bias or late-stage drift.

C3one line summary

TRUST-TAEA is a trustworthiness-guided two-archive evolutionary algorithm using variable-grouping sparse search that outperforms or matches existing methods on large-scale multi-objective benchmarks and a microgrid dispatch problem.

References

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[1] A. Younis, Z. Dong, Adaptive surrogate assisted multi-objective optimization approach for highly nonlinear and complex engineering design problems, Applied Soft Computing 150 (2024) 111065.doi: https: 2024 · doi:10.1016/j.asoc.2023.111065

[2] J. Yang, F. Yan, J. Zhang, C. Peng, R. Zhang, Multi-objective plant root growth optimization algorithm for engineering design problems anduavpathplanning,Chaos,Solitons&Fractals201(2025)117303. doi:ht 2025 · doi:10.1016/j.chaos.2025.117303

[3] A. Pichitkul, A. Boksawat, T. Phiboon, S. Tantrairatn, S. Bureerat, R. Kasemsri, A. Ariyarit, An intelligent multi-objective robust de- sign optimization framework based on pathline transformation and 2026 · doi:10.1016/j.apples.2026.100310

[4] F. Ballestín, R. Blanco, Theoretical and practical fundamentals for multi-objectiveoptimisationinresource-constrainedprojectschedul- ing problems, Computers & Operations Research 38 (1) (2011) 51– 62, 2011

[5] M. Fekri, M. Heydari, M. Mahdavi Mazdeh, Bi-objective optimiza- tion of flexible flow shop scheduling problem with multi-skilled human resources, Engineering Applications of Artificial Intelligence 13 2024 · doi:10.1016/j.engappai.2024

Receipt and verification

First computed	2026-05-18T02:44:48.632644Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

414284fb83566b9f25e6f4b201cbfe6ae80abbfb65c82ceaa5ec5a3dcc35b5b3

Aliases

arxiv: 2605.13324 · arxiv_version: 2605.13324v1 · doi: 10.48550/arxiv.2605.13324 · pith_short_12: IFBIJ64DKZVZ · pith_short_16: IFBIJ64DKZVZ6JPG · pith_short_8: IFBIJ64D

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/IFBIJ64DKZVZ6JPG6SZADS76NL \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 414284fb83566b9f25e6f4b201cbfe6ae80abbfb65c82ceaa5ec5a3dcc35b5b3

Canonical record JSON

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