Dynamic optimal control of greenhouse environment based on improved genetic algorithm
1. School of Agricultural Engineering， Jiangsu University， Zhenjiang， Jiangsu 212013, China; 2. School of Electronics and Information， Jiangsu University of Science and Technology， Zhenjiang， Jiangsu 212003， China
Abstract：To effectively and practically solve the dynamic economic optimal control problem of greenhouse environment with mixed integer variables, an improved genetic algorithm（IGA） with engineering constraint rules was proposed. Based on piecewise constant method for discretizing the control variables, the optimal control problem was transformed into nonlinear programming (NLP) problem with finitedimension parameters, and the standard genetic algorithm (SGA) was used to solve the NLP problem. A precise penalty function was used to deal with the state variable path constraints.The engineering constraint rules and some improvement measures of elite retention, multipopulation parallel evolution and integer variable setting were used to improve the algorithm performance. The simulation results show that compared with SGA, IGA obtains better performance indexes and control quality, which proves the effectiveness and practicability of the proposed method.
晋春， 毛罕平， 马国鑫， 王奇瑞， 石强. 基于改进遗传算法的温室环境动态优化控制[J]. 江苏大学学报（自然科学版）, 2022, 43(2): 169-177.
JIN Chun， MAO Hanping， MA Guoxin， WANG Qirui， SHI Qiang. Dynamic optimal control of greenhouse environment based on improved genetic algorithm[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2022, 43(2): 169-177.
