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

作     者：Gu, Fahui Wang, Wenxiang Lai, Luyan 

作者机构：Jiangxi Appl Technol Vocat Coll Nanchang Jiangxi Peoples R China Jiangxi Environm Engn Vocat Coll Nanchang Jiangxi Peoples R China 

出 版 物：《INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE》 (国际认知信息学与自然智能杂志)

年 卷 期：2019年第13卷第2期

页      面：1-17页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Natural Science Foundation of China Fund of Science and the Technology Planning Project of Guangdong Province of China [2017A010101037] Education Department of Jiangxi Province of China Science and Technology research projects [GJJ151433, GJJ161687, GJJ161688, GJJ161691] 

主　　题：Dual-Population Co-Evolution Teaching Learning Optimization Algorithm Proportion Integration Differentiation Teaching-Learning-Based Optimization Algorithm 

摘      要：The teaching-learning-based optimization (TLBO) algorithm has been applied to many optimization problems, but its theoretical basis is relatively weak, its control parameters are difficult to choose, and it converges slowly in the late period and makes it too early to mature. To overcome these shortcomings, this article proposes a dual-population co-evolution teaching and learning optimization algorithm (DPCETLBO) in which adaptive learning factors and a multi-parent non-convex hybrid elite strategy are introduced for a population with high fitness values to improve the convergence speed of the algorithm, while an opposition-based learning algorithm with polarization is introduced for a population with lower fitness values to improve the global search ability of the algorithm. In a proportion integration differentiation (PID) parameter optimization experiment, the simulation results indicate that the convergence of the DPCETLBO algorithm is fast and precise, and its global search ability is superior to those of the TLBO, ETLBO and PSO algorithms.