关键词: 教学优化算法, 寻优精度, 聚类划分方法, 局部搜索能力

Abstract: To improve the low precision and poor stability of Teaching-Learning-Based Optimization（TLBO） algorithm, a Multi-Classes Interaction Teaching-Learning-Based Optimization（MCITLBO） is proposed. On the basis of TLBO, a new clustering partition based on Euclidean distance is firstly introduced to facilitate the population effectively search in the feasible neighbor region which can strengthen the local search ability of the algorithm and strive a balance between exploration and exploitation. Two information sharing scheme is introduced to improve the learning styles of students so as to increase information exchange in the evolution and avoid the premature convergence and stagnation behavior of subgroups. Numerical experiments on six unconstrained functions, four constrained functions and one engineering design problem called the tension string design problem show that MCITLBO outperforms the other algorithms in precision and stability.

Key words: Teaching-Learning-Based Optimization（TLBO） algorithm, optimization precision, clustering partition method, local search ability