Improved teaching-learning-based optimization algorithm based on fusion difference mutation 1

Abstract

Teaching-learning-based optimization algorithm (TLBO) is a swarm intelligence optimization algorithm that simulates classroom teaching phenomenon. In order to solve the problem that TLBO algorithm is easy to fall into local optimum and has poor stability, an improved teaching-learning-based optimization algorithm based on fusion difference mutation (IDMTLBO) is proposed. Firstly, adaptive teaching factors are introduced. Secondly, in the teaching stage, each student studies according to the gap between himself and the teacher, which improves the convergence speed and convergence accuracy of the algorithm. Finally, in the learning stage, students are divided into two levels according to their learning level, and two students are randomly selected to improve the iterative equation in the learning stage with the difference mutation strategy, It improves the disadvantage that the algorithm is easy to fall into local optimum. Numerical experiments show that the convergence speed and convergence accuracy of the algorithm are obviously better than TLBO algorithm, DMTLBO algorithm, DSTLBO algorithm.