Dynamic Multi-objective Optimization of Chemical Processes Using Modified Bare- Bones MOPSO Algorithm
Dynamic multi-objective optimization is a complex and dimcult research topic of process systems engineering. In this paper. a modified multi-objective bare-bones particle swarm optimization ( MOBBPSO) algorithm is proposed tbat takes advantage of a few parameters of bare-bones algorithm. To avoid pr...
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Published in | 东华大学学报(英文版) Vol. 31; no. 2; pp. 184 - 189 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
2014
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Subjects | |
Online Access | Get full text |
ISSN | 1672-5220 |
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Summary: | Dynamic multi-objective optimization is a complex and dimcult research topic of process systems engineering. In this paper. a modified multi-objective bare-bones particle swarm optimization ( MOBBPSO) algorithm is proposed tbat takes advantage of a few parameters of bare-bones algorithm. To avoid premature convergence. Gaussian mutation is introduced; and an adaptive sampling distribution strategy is also used to improve the exploratory capability. Moreover. a circular crowded sorting approach is adopted to improve the uniformity of the population distribution. Finally. by combining the algorithm with control vector parameterization. an approach is proposed to solve the dynamic optimization problems of chemical processes. It is proved that the new algorithm performs better compared with other classic multiobjective optimization algorithms through the results of solving three dynamic optimization problems. |
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Bibliography: | Dynamic multi-objective optimization is a complex and dimcult research topic of process systems engineering. In this paper. a modified multi-objective bare-bones particle swarm optimization ( MOBBPSO) algorithm is proposed tbat takes advantage of a few parameters of bare-bones algorithm. To avoid premature convergence. Gaussian mutation is introduced; and an adaptive sampling distribution strategy is also used to improve the exploratory capability. Moreover. a circular crowded sorting approach is adopted to improve the uniformity of the population distribution. Finally. by combining the algorithm with control vector parameterization. an approach is proposed to solve the dynamic optimization problems of chemical processes. It is proved that the new algorithm performs better compared with other classic multiobjective optimization algorithms through the results of solving three dynamic optimization problems. 31-1920/N dynamic multi-objective optimization; bare-bones particle swarm optimization(PSO) algorithm; chemical process DU Wen-li, WANG Shan-shan, CHEN Xu, QIAN Feng( Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China) |
ISSN: | 1672-5220 |