关键词: 教与学优化, 早熟, 双种群, 竞争, 反向学习

Abstract: To overcome these weakness of premature, low solution precision and slow convergence speed in solving higher dimension functions, an improved Teaching Learning Based Optimization（TLBO） algorithm with double populations competition is proposed. Two teachers with the best achievement individual are chosen from population and other individuals are divided into two student populations by imperialist competitive. Each teacher guides itself student population. In the iteration, teacher can attract other individual in another population to become his own population member. Opposition-based learning is introduced to improve the learning ability of teacher. Comparison with related algorithms is given on some classical benchmark functions. The results show that the proposed algorithm has better convergence rate and accuracy for numerical optimization, suitable to solve the high dimensional optimization problem.

Key words: teaching learning based optimization, premature, double populations, competition, opposition-based learning