Quadratic interpolation based teaching-learning-based optimization for chemical dynamic system optimization

被引:59
作者
Chen, Xu [1 ,2 ]
Mei, Congli [1 ]
Xu, Bin [3 ]
Yu, Kunjie [4 ]
Huang, Xiuhui [5 ]
机构
[1] Jiangsu Univ, Sch Elect & Informat Engn, Zhenjiang 212013, Jiangsu, Peoples R China
[2] East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China
[3] Shanghai Univ Engn Sci, Sch Mech Engn, Shanghai 201620, Peoples R China
[4] Zhengzhou Univ, Sch Elect Engn, Zhengzhou 450001, Henan, Peoples R China
[5] Univ Shanghai Sci & Technol, Sch Energy & Power Engn, Shanghai 200093, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Dynamic system optimization; Chemical processes; Global optimization; Teaching-learning-based optimization; Quadratic interpolation; CONTROLLED RANDOM SEARCH; DIFFERENTIAL EVOLUTION; GLOBAL OPTIMIZATION; PARAMETER-ESTIMATION; GENETIC ALGORITHM; MULTIOBJECTIVE OPTIMIZATION; APPROXIMATION; PARALLEL; DESIGN; MODELS;
D O I
10.1016/j.knosys.2018.01.021
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Optimal design and control of industrially important chemical processes rely on dynamic optimization. However, because of the highly constrained, nonlinear, and sometimes discontinuous nature that is inherent in chemical processes, solving dynamic optimization problems (DOPs) is still a challenging task. Teaching-learning-based optimization (TLBO) is a relative new metaheuristic algorithm based on the philosophy of teaching and learning. In this paper, we propose an improved TLBO called quadratic interpolation based TLBO (QITLBO) for handling DOP5 efficiently. In the QITLBO, two modifications, namely diversity enhanced teaching strategy and quadratic interpolation operator, are introduced into the basic TLBO. The diversity enhanced teaching strategy is employed to improve the exploration ability, and the quadratic interpolation operator is used to enhance the exploitation ability; therefore, the ensemble of these two components can establish a better balance between exploration and exploitation. To test the performance of the proposed method, QITLBO is applied to solve six chemical DOPs include three parameter estimation problems and three optimal control problems, and compared with eleven well-established metaheuristic algorithms. Computational results reveal that QITLBO has the best precision and reliability among the compared algorithms for most of the test problems. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码:250 / 263
页数:14
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