期刊
KNOWLEDGE-BASED SYSTEMS
卷 145, 期 -, 页码 250-263出版社
ELSEVIER
DOI: 10.1016/j.knosys.2018.01.021
关键词
Dynamic system optimization; Chemical processes; Global optimization; Teaching-learning-based optimization; Quadratic interpolation
资金
- Natural Science Foundation of Jiangsu Province [BK 20160540]
- China Postdoctoral Science Foundation [2016M591783]
- Research Talents Startup Foundation of Jiangsu University [15JDG139]
- National Natural Science Foundation of China [61703268]
- Fundamental Research Funds for the Central Universities [222201717006]
- PAPD of Jiangsu Higher Education Institutions
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.
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