In this paper, a new optimization technique known as Teaching–Learning-Based Optimization (TLBO) is implemented for solving high dimensional function optimization problems. Even though there are several other approaches to address this issue but the cost of computations are more in handling high dimensional problems. In this work we simulate TLBO for high dimensional benchmark function optimizations and compare its results with very widely used alternate techniques like Differential Evolution (DE) and Particle Swarm Optimization (PSO). Results clearly reveal that TLBO is able to address the computational cost issue for all simulated functions to a large dimensions compared to other two techniques.