Strengthened teaching–learning-based optimization algorithm for numerical optimization tasks

The teaching–learning-based optimization algorithm (TLBO) is an efficient optimizer. However, it has several shortcomings such as premature convergence and stagnation at local optima. In this paper, the strengthened (STLBO) proposed to enhance basic TLBO’s exploration exploitation properties by introducing three strengthening mechanisms: linear increasing teaching factor, elite system composed of new teacher class leader, Cauchy mutation. Subsequently, seven variants STLBO are designed based on combined deployment improved mechanisms. Performance novel STLBOs evaluated implementing them thirteen numerical tasks, including unimodal tasks (f1–f7) six multimodal (f8–f13). results show that STLBO7 top list, significantly better than original TLBO. Moreover, remaining also outperform Finally, a set comparisons implemented between other advanced techniques, HS, PSO, MFO, GA HHO. curves prove clearly outperforms competitors, stronger optimal avoidance, faster speed higher solution accuracy. All above manifests search performance Data Availability Statements: data generated or analyzed during study included in published article (and its supplementary information files).