Dynamic optimization of industrial processes with nonuniform discretization-based control vector parameterization

Xu Chen, Wenli Du, Huaglory Tianfield, Rongbin Qi, Wangli He, Feng Qian

    Research output: Contribution to journalArticlepeer-review

    47 Citations (Scopus)


    This paper proposes a novel scheme of nonuniform discretizetion-based control vector parameterization (ndCVP, for short) for dynamic optimization problems (DOPs) of industrial processes. In our ndCVP scheme, the time span is partitioned into a multitude of uneven intervals, and incremental time parameters are encoded, along with the control parameters, into the individual to be optimized. Our coding method can avoid handling complex ordinal constraints. It is proved that ndCVP is a natural generalization of uniform discretization-based control vector parameterization (udCVP). By integrating ndCVP into hybrid gradient particle swarm optimization (HGPSO), a new optimization method, named ndCVP-HGPSO for short, is formed. By application in four classic DOPs, simulation results show that ndCVP-HGPSO is able to achieve similar or even better performances with a small number of control intervals; while the computational overheads are acceptable. Furthermore, ndCVP and udCVP are compared in terms of two situations: given the same number of control intervals and given the same number of optimization variables. The results show that ndCVP can achieve better performance in most cases.
    Original languageEnglish
    Pages (from-to)1289-1299
    Number of pages11
    JournalIEEE Transactions on Automation Science and Engineering
    Issue number4
    Publication statusPublished - Oct 2014


    • dynamic optimization
    • HGPSO
    • computational efficiency
    • ndCVP
    • vector parameterization


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