Research On Teaching Learning Based Optimization Algorithm And Application Of Multi-strategy Improvement

Teaching-learning-based optimization algorithm is a new type of swarm intelligent optimization algorithm,which seeks the optimal solution of problems by simulating the teaching process between teachers and students in real life.It has the advantages of simple structure,few parameters and easy programming.However,with the deepening of the research,some scholars found that the algorithm has some shortcomings,such as low optimization accuracy,easy to fall into local optimal,slow convergence speed,etc.This thesis mainly aims to improve the shortcomings of Teaching-Learning-Based Optimization algorithm,and apply the improved algorithm to the actual optimization problem,in order to further develop the theory of Teaching-Learning-Based Optimization algorithm and expand its application scope.The work content of this thesis is mainly divided into the following three aspects:(1)In order to solve the unreasonable setting of teaching factor parameters and improve the ability of the algorithm to jump out of the local optimal,a teachinglearning-based optimization algorithm based on Morlet wavelet variation was proposed by introducing adaptive teaching factor and Morlet wavelet variation strategy.The improved algorithm is applied to 18 benchmark test functions and PID controller optimization problems,and compared with 5 swarm intelligent optimization algorithms.Experimental results show that the optimization performance and convergence of the proposed algorithm are better than those of the other five comparison algorithms.(2)In order to improve the stability and global optimization ability of the algorithm,an improved teaching-learning-based optimization algorithm was proposed by using group teaching and autonomous learning strategies.The improved algorithm is applied to optimize the UAV path planning problem,and compared with the other three population intelligent optimization algorithms,the experimental results show that the improved method can give the optimal flight path of the UAV.(3)In order to balance the ability of local search and global search and improve the accuracy of the algorithm,a chaotic sine and cosine teaching-learning-based optimization algorithm is proposed.The improved algorithm is applied to optimize the multi-threshold image segmentation problem,and compared with the other four population intelligent optimization algorithms,the experimental results show that the image threshold obtained by the algorithm is the best.