The transmission network planning problem has high dimensionality, many variables and complex constraints, which makes the problem difficult to solve. This paper uses a new intelligent algorithm teaching-learning-based optimization (TLBO) to solve the problem. The proposed teaching-learning optimization algorithm has the advantages of fast convergence speed and few setting parameters, but it is easy to fall into the local optimal solution when solving. By adding the independent learning link and reflection link and the adaptive disturbance strategy, this paper improves the ability of the algorithm to find the global optimal solution, and adapts it to the solution of large-scale transmission network planning problem. The transmission network planning model with the objective function is the sum of line investment cost, network loss cost and overload cost. Through the calculation in the Garver-6 node system and the IEEE-18 node system, the correctness and effectiveness of the algorithm applied to the transmission network planning are verified.
