Improved Teaching-Learning-Based Optimization Algorithm and its Application in PID Parameter Optimization

Introduction

The Teaching-Learning-Based Optimization (TLBO) algorithm was first proposed by Rao et al. in 2011 and is a new intelligent optimization algorithm¹. The TLBO algorithm is inspired by educational activities in people’s lives. To try to simulate the “teaching” and “learning” processes in educational activities, the TLBO algorithm performs individual evolution in two “teaching” and “learning” stages. The TLBO algorithm has a simple design with few parameters and is easy to implement. It achieves high speed, high precision and strong convergence capability. In the few short years since the TLBO algorithm was put forward, scholars, such as Rao², have conducted a large amount of performance improvement research. Undoubtedly, this research promoted the rapid development of the TLBO algorithm.

The TLBO algorithm has been widely used in heat exchangers, thermoelectric refrigerators, mechanical design, reactive power distribution networks, and continuous large-scale nonlinear optimization. It shows strong local search ability and convergence performance in these specific applications because of its superior performance. The TLBO algorithm has become an area of interest in the field of intelligent optimization algorithms².

However, due to the short development history of the TLBO algorithm, there are many aspects that require further research. For example, the theoretical foundation is relatively weak, the control parameters are difficult to choose, and the convergence in the late period is slow and premature^3,4. This paper presents some research on the improvement of the TLBO algorithm and introduces the dual-population co-evolution model for the improvement of the TLBO algorithm. The convergence speed of the algorithm is improved by introducing adaptive learning factors and a multi-parent non-convex hybrid elite strategy. A reverse learning algorithm with polarization is introduced to improve the global search capability of the algorithm. In this paper, the improved dual-population co-evolution TLBO (DPCETLBO) algorithm is applied to proportion integration differentiation (PID) control parameter optimization. The experiment shows that the DPCETLBO algorithm can effectively solve the problem of PID parameter optimization with superior performance compared to other algorithms.