Multi-step optimal control of complex process: a genetic programming strategy and its application

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Chen:2004:EAAI,
  author =       "Xiaofang Chen and Weihua Gui and Yalin Wang and 
                 Lihui Cen",
  title =        "Multi-step optimal control of complex process: a
                 genetic programming strategy and its application",
  journal =      "Engineering Applications of Artificial Intelligence",
  year =         "2004",
  volume =       "17",
  pages =        "491--500",
  number =       "5",
  keywords =     "genetic algorithms, genetic programming, Multi-step
                 comprehensive evaluation, Fitness function, Process
                 optimal control",
  ISSN =         "0952-1976",
  URL =          "http://www.sciencedirect.com/science/article/B6V2M-4CMHSNB-1/2/5c02b126719099d090f4dba0eaaa5cea",
  DOI =          "doi:10.1016/j.engappai.2004.04.018",
  owner =        "wlangdon",
  abstract =     "In many industrial processes, especially chemistry and
                 metallurgy industry, the plant is slow for feedback and
                 data test because of complex and varying factors.
                 Considering the multi-objective feature and the complex
                 problem of production stability in optimal control,
                 this paper proposed an optimal control strategy based
                 on genetic programming (GP), used as a multi-step state
                 transferring procedure. The fitness function is
                 computed by multi-step comprehensive evaluation
                 algorithm, which provides a synthetic evaluation of
                 multi-objective in process state based on single
                 objective models. The punishment to process state
                 variance is also introduced for the balance between
                 optimal performance and stability of production. The
                 individuals in GP are constructed as a chain linked by
                 a few relation operators of time sequence for a
                 facilitated evolution in GP with compact individuals.
                 The optimal solution gained by evolution is a
                 multi-step command program of process control, which
                 not only ensures the optimisation tendency but also
                 avoids violent process variation by adjusting control
                 parameters step by step. An optimal control system for
                 operation direction is developed based on this strategy
                 for imperial smelting process in Shaoguan. The
                 simulation and application results showed its
                 effectiveness for production objects optimisation in
                 complex process control.",
}

Genetic Programming entries for Xiaofang Chen Weihua Gui Yalin Wang Lihui Cen

Citations