ZIVARI-TLBO is a grouped Teaching-Learning-Based Optimization (TLBO) method that enhances 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, replacing the receiver's worst eligible learner only when the stored objective value is better. Since the relay precisely 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, along with 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 achieves 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 shows no significant difference from HHO after Holm adjustment. The 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 does not support universal state-of-the-art, global-convergence, engineering-dominance, or CEC superiority claims.
Blogger's Review: ZIVARI-TLBO effectively enhances TLBO's performance by introducing an inter-group elite relay mechanism, showing good competitiveness across multiple dimensions. While it does not surpass WOA in some cases, its zero-cost attributes provide new insights for optimization algorithms, warranting further research and practical validation.