Teaching-learning-based optimization (TLBO) is a recently proposed meta-heuristic optimization method and demonstrates outstanding performance for solving numerous sciences and engineering problems. However, many studies have shown that TLBO has a strong bias towards converging to origin and poorly performs in the problems with shifted solutions. To conquer this problem, a novel self-adaptive hybrid self-learning based TLBO (SHSLTLBO) is proposed in this paper. By constructing a self-adaptive framework, the original TLBO is fused with the Gaussian distribution, and the novel updating law switches in two modes corresponding to the fitness during the searching process. Additionally, in order to avoid local convergence in the initialization, a self-learning phase is introduced, associated to the original teaching and learning phase. The performance of the proposed SHSLTLBO is tested in the numerical benchmark functions, where the comprehensive comparison carries out with the state-of-the-art TLBO variants and other meta-heuristic approaches. The results demonstrate superior advantages of the proposed algorithm at balancing along the evolutionary stages among the TLBO variants. In the experiments, the two well-performed meta-heuristic optimization methods, namely LSHADE and HCLPSO, are used as the comparative methods to SHSLTLBO, where their superior performances of them are shown on the shifted problems. With increasing dimensionality, the performance of TLBO goes down rapidly while LSHADE's relative ranking improves. Apart from these scenarios, the comparative results of different dimensional problems indicate that the proposed SHSLTLBO has the best convergence and stability in solving all 28 benchmarks with low dimension and shifted solutions.

基于教学的优化（TLBO）是最近提出的元启发式优化方法，它在解决众多科学和工程问题方面表现出出色的性能。但是，许多研究表明，TLBO在收敛到原点方面有很大的偏见，并且在解决方案移位问题上表现不佳。为了克服这一问题，本文提出了一种新型的自适应混合自学习TLBO（SHSLTLBO）。通过构建自适应框架，将原始TLBO与高斯分布融合，并且新颖的更新定律在搜索过程中根据适应性以两种模式切换。另外，为了避免初始化中的局部收敛，引入了与原始教学阶段相关的自学习阶段。拟议的SHSLTLBO的性能已在数字基准函数中进行了测试，其中使用最新的TLBO变体和其他元启发式方法进行了全面比较。结果证明了该算法在TLBO变体之间的进化阶段平衡方面的优越性。在实验中，使用了LSHADE和HCLPSO这两种性能良好的元启发式优化方法作为SHSLTLBO的比较方法，在转移的问题上它们表现出了优异的性能。随着尺寸的增加，TLBO的性能迅速下降，而LSHADE的相对排名有所提高。除了这些情况之外，