关键词: 教与学优化算法, Chebyshev映射, 动态自适应学习, 共轭梯度法, 二次学习

Abstract: Teaching-learning based optimization (TLBO) algorithm can solve the complex optimization problems by simulating teaching and learning, and has been applied in many fields. To overcome the weakness of premature, low precision of solution, this paper proposes an improved hybrid chaotic and conjugate gradient method TLBO. This improved algorithm initializes its population by Chebyshev mapping to improve bestrow rate of solution space. To keep the diversity of population, this paper introduces a dynamic learning coefficient to make that students can mainly learn from teacher in early, and improves gradually the ability of affecting itself evolvement of student??s knowledge. In its iteration, teacher individual executes conjugate gradient methods after “teaching” and “learning”. If the states of those student indivduals which are worst can not be changed, second study based on OBL (opposition-based learning) and GL (Gaussian learning) will be executed to them. Finally, some experiments on test functions show that the improved TLBO algorithm has higher precision and better global convergence than TLBO, fitting to solve higher dimension optimization functions.

Key words: teaching-learning based optimization, Chebyshev mapping, dynamic self-adaptive learning, conjugate gradient method, second study