Hybrid gradient particle swarm optimization for dynamic optimization problems of chemical processes

Xu Chen, Wenli Du*, Rongbin Qi, Feng Qian, Huaglory Tianfield

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)708-720
Number of pages13
JournalAsia-Pacific Journal of Chemical Engineering
Issue number5
Early online date30 Jan 2013
Publication statusPublished - Sep 2013


  • control vector parameterization
  • dynamic optimization
  • gradient-based algorithms
  • industrial process optimization
  • particle swam optimization

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Renewable Energy, Sustainability and the Environment
  • Waste Management and Disposal


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