Teaching-Learning-Based Optimization Algorithm with Social Psychology Theory

Abstract

Teaching-learning-based optimization (TLBO) is a heuristic optimization algorithm that simulates the teaching process. In view of the low precision and poor stability of TLBO algorithm, an improved teaching-learning-based optimization algorithm named SPTLBO (social psychology teaching-learning-based optimization) is proposed. Human psychological factors are considered in the improvement of the algorithm. In the “teaching” stage, combining the “expectation effect” theory in social psychology, teachers adopt one-to-one teaching strategy for students with high expectations, which makes outstanding students approach teachers faster. According to cognitive style, students can be divided into two types, “field independence” and “field dependence”, so that it can preserve the diversity of students. Different types of students will adopt different communication methods to learn. After the “teaching” and “learning” stages, combined with the theory of self-regulation, students enter the stage of learning method adjustment. It can enhance the ability of self-exploration and improve the overall level of students. In addition, an adaptive student update factor is introduced to simulate the influence of environment on students’ learning efficiency, which increases the global search ability of the algorithm and avoids falling into local optimum in the initial iteration. The test of 25 test functions shows that, compared with the basic TLBO algorithm and other intelligent optimization algorithms, the SPTLBO algorithm has more advantages in the optimization accuracy and convergence speed.

Keywords

• |teaching-learning-based optimization algorithm (tlbo)|social psychology|expectation effect|field independence-field dependence|self-regulation theory|adaptive update factor