Large-scale multi-objective optimization problems (LSMOPs) remain challenging due to the high-dimensional decision spaces, complex variable interactions, and limited function evaluation budgets, which make it difficult to balance the 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 two-archive evolutionary algorithm. Archive trustworthiness is defined by integrating evolutionary progress with convergence-archive maturity, and is used to coordinate variable-grouping sparse search, anchor-probing compensatory search, and archive stabilization. TRUST-TAEA is evaluated on the LSMOP benchmark suite with 500--5000 decision variables and 2, 3-objectives. Experimental results show that TRUST-TAEA achieves superior and highly competitive performance in terms of convergence, diversity, and stability. A three-objective day-ahead scheduling case of a grid-connected microgrid further demonstrates its practical applicability, where TRUST-TAEA obtains the best IGD$^+$ value and generates a feasible dispatch strategy balancing cost, emissions, and grid-power fluctuation.

翻译：大规模多目标优化问题因高维决策空间、复杂变量交互以及有限的目标函数评估预算而具有挑战性，这使得收敛性、多样性与稳定性之间的平衡难以实现。现有双存档进化算法虽能缓解收敛性与多样性的冲突，但往往未充分利用存档可靠性与问题结构信息，导致搜索效率低下、前端覆盖不完整以及后期存档漂移。为此，本文提出TRUST-TAEA——一种置信度引导的双存档进化算法。通过整合进化进程与收敛存档成熟度定义存档置信度，并用于协调变量分组稀疏搜索、锚点探针补偿搜索与存档稳定化策略。在变量维度为500至5000、目标数为2和3的LSMOP基准测试集上，实验结果表明TRUST-TAEA在收敛性、多样性与稳定性方面均取得优越且极具竞争力的性能。针对并网微电网的三目标日前调度案例进一步验证其实用价值：TRUST-TAEA获得最优IGD$^+$值，并生成平衡成本、排放与电网功率波动的可行调度方案。