文献类型：期刊文献

英文题名：An improved teaching-learning-based optimization algorithm for numerical and engineering optimization problems

作者：Yu, Kunjie[1];Wang, Xin[2];Wang, Zhenlei[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Shanghai Jiao Tong Univ, Ctr Elect & Elect Technol, Shanghai 200240, Peoples R China

年份：2016

卷号：27

期号：4

起止页码：831

外文期刊名：JOURNAL OF INTELLIGENT MANUFACTURING

收录：;EI(收录号：20143600013856);WOS:【SCI-EXPANDED(收录号:WOS:000379230800010)】；

基金：This research was supported by Major State Basic Research Development Program of China under Grant No. 2012CB720500, National Natural Science Foundation of China under Grant Nos. 61333010, 61222303, Fundamental Research Funds for the Central Universities, National High-Tech Research and Development Program of China under Grant No. 2013AA040701, National Key Scientific and Technical Project of China under Grant No. 2012BAF05B00, Shanghai R&D Platform Construction Program under Grant No. 13DZ2295300, and Open Research Fund of State Key Laboratory of Synthetical Automation for Process Industries.

语种：英文

外文关键词：Improved teaching-learning-based optimization; Differential evolution; Chaotic perturbation; Unconstrained optimization; Constrained optimization

摘要：The teaching-learning-based optimization (TLBO) algorithm, one of the recently proposed population-based algorithms, simulates the teaching-learning process in the classroom. This study proposes an improved TLBO (ITLBO), in which a feedback phase, mutation crossover operation of differential evolution (DE) algorithms, and chaotic perturbation mechanism are incorporated to significantly improve the performance of the algorithm. The feedback phase is used to enhance the learning style of the students and to promote the exploration capacity of the TLBO. The mutation crossover operation of DE is introduced to increase population diversity and to prevent premature convergence. The chaotic perturbation mechanism is used to ensure that the algorithm can escape the local optimal. Simulation results based on ten unconstrained benchmark problems and five constrained engineering design problems show that the ITLBO algorithm is better than, or at least comparable to, other state-of-the-art algorithms.