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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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