Abstract:

Concerning the problem that the teaching learning based optimization (TLBO) algorithm is easy to premature with low solution precision, we propose an improved TLBO algorithm with selfstudy of teachers and optionalstudy of students. In every iteration, individual teachers adopt the oppositionbased learning (OBL) to generate an opposition search population, and the search space of the algorithm is guided to approximate optimum space. This mechanism is helpful for improving the balance and exploring the ability of the TLBO. Every individual student  executes OBL randomly and studies from teachers at the same time. For keeping the diversity of the population, we calculate the students' jumping probability to current teachers. We adopt the roulette mechanism to choose the individuals which will replace the parent individuals. Compared with related algorithms, the simulations on 11 classical benchmark functions show that the proposed algorithm has better convergence rate and accuracy for numerical optimization, and is suitable for solving high dimensional optimization problem.

Key words: teaching learning based optimization;premature;self-study;opposition-based learning;simulated anneal