An improved teaching-learning-based optimization
作者单位：Robotics and Microsystems Center Jiangsu Provincial Key Laboratory of Advanced Robotics and Collaborative Innovation Center of Suzhou Nano Science and Technology Soochow University
会议名称：《第37届中国控制会议》
会议日期：2018年
学科分类：08[工学] 081104[工学-模式识别与智能系统] 0811[工学-控制科学与工程]
基 金：supported by National Natural Science Foundation(NNSF)of China under Grant 51675358
关 键 词：Teaching-learning-based optimization Second-teaching strategy Teacher’s self-exploration study
摘 要：Teaching-learning-based optimization（TLBO） is a new proposed heuristic algorithm for optimization applications in recent years. In this paper, an improved TLBO algorithm（ITLBO） is presented. In the teacher phase, the second-teaching strategy and self-exploration study of teacher are introduced to improve the convergence speed. And the improved learner phase can ensure the diversity of the population to avoid the possibility of falling into a local optimum. Meanwhile, second-teaching strategy and the improved learner phase enable the algorithm to use fine local search and improve the precision. To assess the performance of ITLBO algorithm, experiments are implemented on 8 classical benchmark functions. The result show that ITLBO algorithm is an effective approach.