Teaching learning based optimization is a new type of swarm intelligence optimization algorithm that simulates class teachingphenomena. With simple parameters?and fast convergence speed,the algorithm has been widely used in function optimization,engineeringcalculation and other fields. However,the algorithm tends to fall into local convergence later,so the modified teaching learning based optimization ( MTLBO) with additional memory strategy is proposed. The teachers’ memory strategy is added in the teaching stage and thestudents爷 historical memory knowledge and teachers’ historical teaching ability play an important role in improving
?the overall teachinglevel of the class. When updating learners each time, the optimal value of the previous generation and the current optimal value ofteachers are considered, effectively enhancing the local search ability of the algorithm. In the learning stage, the individual learningstrategies are added to the optimal individual?
and random individual,so that multiple students can learn from each other and make full useof the knowledge information in the class,thus enhancing the global search ability of the algorithm. The proposed algorithm is simulatedby multiple test functions with different characteristics, and compared with the basic TLBO algorithm and two improved TLBOalgorithms. It is showed that the proposed MTLBO algorithm not only achieves higher convergence accuracy and stability, but alsoimproves the convergence speed,effectively avoiding the local convergence of the algorithm.