For that the Teaching-Learning-Based Optimization （TLBO） algorithm has some problems， such as prematurity and poor solution accuracy， in solving high-dimensional optimization problems， an Improved TLBO algorithm with Adaptive Competitive learning （ITLBOAC） was proposed. Firstly， a weighted parameter with nonlinear change was introduced into the “teaching” operator to determine the ability of the current individual to maintain its own state and adjust the attitude of the current individual towards learning from teachers. As a result， the current individual learnt more from the teacher in the early stage to improve its own state quickly， and kept the state of itself more in the later stage to slow down the influence of the teacher on it. Then， based on ecological cooperation and competition mechanisms， a “learning” operator based on adaptive competition between nearest neighbor individuals was introduced. To make the current individual chose its near neighbors and the individuals eventually shifted from cooperative evolution to competitive learning. Test results on 12 Benchmark test functions show that compared with four improved TLBO algorithms， the proposed algorithm is better in terms of accuracy of solutions， stability and convergence speed， and is much better than TLBO algorithm at the same time， which verify that the proposed algorithm is suitable for solving high-dimensional continuous optimization problems. Test results with compression spring and three-bar truss design problems selected to test show that the optimal values obtained by ITLBOAC decreased by 3.03% and 0.34% respectively， compared with those obtained by TLBO algorithm. It can be seen that ITLBOAC is a trustworthy algorithm in solving constrained engineering optimization problems.