文献类型：期刊文献

英文题名：Constrained optimization based on improved teaching-learning-based optimization algorithm

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

机构：[1]E 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：352

起止页码：61

外文期刊名：INFORMATION SCIENCES

收录：;EI(收录号：20161302170532);WOS:【SCI-EXPANDED(收录号:WOS:000375632000005)】；

基金：This research was supported by National Key Scientific and Technical Project of China under grant no. 2015BAF22B02, National Natural Science Foundation of China under grant no. 61403141, Shanghai Natural Science Foundation under grant no. 14ZR1421800, and the State Key Laboratory of Synthetical Automation for Process Industries under grant no. PAL-N201404.

语种：英文

外文关键词：Teaching-learning-based optimization; Constrained optimization; Constraint handling; Learning strategy

摘要：This paper proposes an improved constrained teaching-learning-based optimization (ICTLBO) method to efficiently solve constrained optimization problems (COPs). In the teacher phase of ICTLBO, the population is partitioned into several subpopulations, and the direction information between the mean position of each subpopulation and the best position of population guide the corresponding subpopulation to the promising region promptly. Information exchange between different subpopulations is used to discourage premature convergence of each subpopulation. Furthermore, in the learner phase, a new learning strategy is introduced to improve the population diversity and enhance the global search ability. Three different constraint handling methods are adopted for three situations, which are infeasible, semi-feasible, and feasible situations, during the evolution process. To evaluate the performance of ICTLBO, 22 benchmark functions presented in CEC2006 and 18 benchmark functions introduced in CEC2010 are chosen as the test suite. Moreover, four widely used engineering design problems are selected to test the performance of ICTLBO for real-world problems. Experimental results indicate that ICTLBO can obtain a highly competitive performance compared with other state-of-the-art algorithms. (C) 2016 Elsevier Inc. All rights reserved.Inc