A modified teaching-learning-based optimization algorithm (MTLBO) was proposed to solve the shortcomings of standard teaching-learning-based optimization algorithm (TLBO), such as low optimization accuracy, slow convergence speed and weak avoidance of local optimization. In the teaching and learning stages of the TLBO, the nonlinear convergence factor adjustment and benchmarking management strategies were introduced respectively. Based on the random combination of the two strategies, three different MTLBOs were constructed. Subsequently, the experimental results show that the three MTLBOs are better than the TLBO. Among them, the MTLBO3 with the two modified strategies achieves the best numerical results, which is much better than the original TLBO. In order to further verify the effectiveness of the proposed algorithm, numerical experiments are carried out with other well-known swarm intelligence optimization algorithms. Numerical results and convergence curves show that the optimization performance of MTLBO3 is significantly better than other comparison methods, with higher solution accuracy, faster convergence speed and better local optimization avoidance ability. Finally, the effectiveness of the proposed algorithm is further verified using constrained engineering optimization problems.