{"paper":{"title":"TRUST-TAEA: A trustworthiness-guided two-archive evolutionary algorithm with variable-grouping sparse search for large-scale multi-objective optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"TRUST-TAEA defines trustworthiness from evolutionary progress and archive maturity to coordinate variable-grouping sparse search in large-scale multi-objective optimization.","cross_cats":["cs.NE"],"primary_cat":"math.OC","authors_text":"JunYi Cui","submitted_at":"2026-05-13T10:36:02Z","abstract_excerpt":"Large-scale multi-objective optimization remains challenging because high-dimensional decision spaces, complex variable interactions, and limited function evaluation budgets make it difficult to balance convergence, diversity, and stability. Existing two-archive evolutionary algorithms can alleviate the conflict between convergence and diversity, but they often underuse archive reliability and problem-structure information, leading to inefficient search, incomplete front coverage, and late-stage archive drift. To address these issues, this paper proposes TRUST-TAEA, a trustworthiness-guided tw"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"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.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"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.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"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.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"TRUST-TAEA defines trustworthiness from evolutionary progress and archive maturity to coordinate variable-grouping sparse search in large-scale multi-objective optimization.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"8020834a106b80101a411efd2ba1f906f880665ab72785c50e764cecbd8b984a"},"source":{"id":"2605.13324","kind":"arxiv","version":1},"verdict":{"id":"c2c0feb3-da72-4041-bb04-53b62a588002","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T17:46:37.565504Z","strongest_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.","one_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.","pipeline_version":"pith-pipeline@v0.9.0","weakest_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.","pith_extraction_headline":"TRUST-TAEA defines trustworthiness from evolutionary progress and archive maturity to coordinate variable-grouping sparse search in large-scale multi-objective optimization."},"references":{"count":41,"sample":[{"doi":"10.1016/j.asoc.2023.111065","year":2024,"title":"A. 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