关键词: 教学优化算法, 矩形邻域结构, 邻域层数, 边界信息, 个体扰动, 种群多样性

Abstract: A teaching-learning-based optimization algorithm with rectangle neighborhood structure (RNTLBO) is proposed to overcome the shortcomings of low global search precision and premature convergence of the original teaching-learning-based optimization algorithm (TLBO) while handling complex multimodal functions. In the algorithm, the population space is designed as a rectangular structure, and the individual rectangular neighborhood is determined by the rectangle thickness and the individual rectangular region surrounding it. In both teaching and learning stages, the optimal individual in the neighborhood is used to guide the search, which strengthens the ability of the algorithm to explore new solutions and exploit local optimal solutions.In order to prevent the algorithm from falling into the local optimum prematurely, the individual perturbation stage guided by search boundary information is added, so that the population can maintain good diversity even in the later evolution stage. The simulation results of complex functions with shift and rotation show that the new algorithm is superior to the original TLBO and some other recently improved variants in terms of accuracy and stability in most cases.

Key words: teaching-learning-based optimization (TLBO), rectangle neighborhood structure, layer number of neighborhood, boundary information, individual disturbance, population diversity