ZIVARI-TLBO is a grouped Teaching-Learning-Based Optimization (TLBO) method that augments an existing population-state controller with a fixed inter-group evaluated-elite relay. At each scheduled event, every group offers its already evaluated elite to the next group in a fixed ring; the elite replaces the receiver's worst eligible learner only when its stored objective value is better. Because the exact relay copies an already evaluated solution and its stored fitness, it requires no additional objective-function calls. The frozen gts-v4-cm-fixed implementation is evaluated under equal 10,000-evaluation budgets on eight classical functions at dimensions 10, 30, 50, and 100, with 30 matched seeds, and on five constrained engineering problems. A direct ablation against the same grouped landscape-aware controller without relay records 728/11/221 wins/ties/losses and a rank-biserial effect size of 0.624 across dimensions. In an eight-method multidimensional comparison, WOA obtains the best average rank (2.914) and ZIVARI-TLBO ranks second (3.382); ZIVARI-TLBO significantly outperforms TLBO, MCTLBO, DE, PSO, and GWO, loses significantly to WOA, and is not significantly different from HHO after Holm adjustment. Feasibility-aware engineering results are mixed and sensitive to the current static-penalty formulation. The evidence supports a scoped relay contribution and budget-consistent information-sharing mechanism, but not universal state-of-the-art, global-convergence, engineering-dominance, or CEC superiority claims.

翻译：ZIVARI-TLBO是一种分组式教学优化算法（TLBO），通过固定组间评估精英中继机制增强现有种群状态控制器。在每次预定事件中，每个分组将其已评估的精英个体沿固定环状结构传递给下一组；仅当该精英的存储目标值更优时，才替代接收组中最差合格学习者的位置。由于精确中继复制的是已评估解及其存储的适应度值，因此无需额外目标函数调用。该冻结版gts-v4-cm-fixed实现方法在8个经典测试函数（维度分别为10、30、50和100）上以10,000次评估预算及30组匹配随机种子进行验证，并应用于5个约束工程问题。通过直接消融实验，相较于不含中继机制的同类分组景观感知控制器，在跨维度实验中取得728胜/11平/221负的战绩，秩双列效应量为0.624。在八种方法的多维比较中，WOA获得最佳平均排名（2.914），ZIVARI-TLBO位列第二（3.382）；经Holm校正后，ZIVARI-TLBO显著优于TLBO、MCTLBO、DE、PSO和GWO，显著逊于WOA，与HHO无显著差异。工程问题的可行性感知结果呈现混合态势，且对当前静态罚函数形式敏感。实验证据支持该算法在限定场景下的中继贡献和预算一致的信息共享机制，但不足以证明其在通用最优性、全局收敛性、工程主导性或CEC竞赛优势方面的绝对优越性。