期刊
ASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING
卷 8, 期 5, 页码 708-720出版社
WILEY
DOI: 10.1002/apj.1712
关键词
dynamic optimization; particle swam optimization; gradient-based algorithms; control vector parameterization; industrial process optimization
资金
- Major State Basic Research Development Program of China [2012CB720500]
- National Natural Science Foundation of China [61134007, 21276078, 21206037]
- National Science Fund for Outstanding Young Scholars [61222303]
- Fundamental Research Funds for the Central Universities
Dynamic optimization problems (DOP) in chemical processes are very challenging because of their highly nonlinear, multidimensional, multipeak and constrained nature. In this paper, we propose a novel algorithm named hybrid gradient particle swarm optimization (HGPSO) by hybridizing particle swarm optimization (PSO) with gradient-based algorithms (GBA). HGSPO can improve the convergence rate and solution precision of pure PSO, and avoid getting trapped to local optimums with pure GBA search. We further incorporate HGPSO into control vector parameterization (CVP), a method converting DOP into nonlinear programming, to solve five complex DOPs. These DOPs include multimodal, multidimensional and constrained problems. The experiments demonstrate that HGPSO performs much better in terms of solution precision and computational cost when compared with other PSO variants. (c) 2013 Curtin University of Technology and John Wiley & Sons, Ltd.
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