Teaching-learning-based optimization with variable-population scheme and its application for ANN and global optimization

A teaching-learning-based optimization algorithm (TLBO) which uses a variable population size in the form of a triangle form (VTTLBO) is proposed in the paper. The main goal of the proposed method is to decrease the computing cost of original TLBO and extend it for optimizing the parameters of artificial neural network (ANN). In the proposed algorithm, the evolutionary process is divided into some equal periods according to the maximal generation. The population size in each period is changed in form of triangle. In the linear increasing phase of population's number, some new individuals are generated with gauss distribution by using the adaptive mean and variance of the population. In the linear decreasing phase of population's number, some highly similar individuals are deleted. To compare the performance of the proposed method with some other methods, Saw-tooth teaching-learning-based optimization algorithm is also designed with simulating the basic principle of Saw-tooth genetic algorithm (STGA), and some other EAs with the fixed population size are also simulated. A variety of benchmark problems and system modeling and prediction problems with ANN are tested in this paper, the results indicate that the computation cost of the given method is small and the convergence accuracy and speed of it are high.