Improved teaching–learning-based optimization algorithm with group learning

Abstract

The typical teaching–learning-based optimization (TLBO) algorithm tends to devolve into local optimization and suffers from the rapid loss of population diversity. In this study, an improved TLBO algorithm with group learning (GTLBO) is established to solve these problems. In the proposed algorithm, a class is divided into several groups. The individual with the highest level is selected as the teacher for each group. Then, the teacher implements the TLBO algorithm in each group. This strategy of group learning can maximize the time before the students reach the teacher’s level and effectively ensure population diversity. Given an effectively diverse population, the idea of reversing the beginning and ending is introduced to boost the convergence rate of the algorithm. Moreover, a matrix displacement method is provided to solve the premature termination phenomenon of the algorithm. Finally, the performance of the GTLBO is investigated across six complex high-dimensional benchmark functions. Results obtained through experiments show that the GTLBO conduces enhanced performance in solving problems of multimodal function optimization. The convergence speeds and solution accuracy of the proposed algorithm are significantly improved compared with those of the typical TLBO algorithm.