关键词: 教与学优化算法, 拉普拉斯分布, 鲍德温学习效应, 无人机航路规划

Abstract: Teaching-Learning-Based Optimization (TLBO) algorithm is a powerful evolutionary algorithm that has recently been widely applied in a variety of optimization problems.However,in the later period of evolution of the TLBO algorithm,the diversity of learners will be degraded with the increasing iteration of evolution and the smaller scope of solutions,which leads to trap in local optima and premature convergence.For this reason,an improved version of TLBO algorithm based on Laplace distribution and Balwin learning effect (LBTLBO) was presented.Laplace distribution was used to expand exploration space,and the Balwin learning effect was applied to make good use of experience information to identify more promising solutions to make the algorithm more competitive.The experimental performances verified that LBTLBO algorithm enhanced the solution accuracy and quality compared to original TLBO,TLBO algorithm based on Laplace distribution (LTLBO),TLBO algorithm based on Balwin learning effect (BTLBO) as well as various versions of TLBO.LBTLBO was used to solve Unmanned Aerial Vehicle(UVA)path planning problem.The simulation experiments showed that the improved algorithm could get more accurate path and convergence rate in the UVA path planning problem.

Key words: teaching-learning-based optimization, Laplace distribution, Balwin learning effect, unmanned aerial vehicle path planning