湖南农业大学学报（自然科学版）2017，Vol.43Issue(2)：217-221,5.DOI:10.13331/j.cnki.jhau.2017.02.019
基于灰色粒子群算法的温室环境多目标优化控制
Multi–objective function optimization for environmental control in greenhouse based on grey particle swarm algorithm
摘要
Abstract
On the basis of the extended autoregressive model (ARX), a multi–objective function of the temperature, the humidity and the economic cost was established by introducing the artificial control factors. Multi–objective fuction optimization for environmental control was carried out in the tea–growing seedling greenhouse by using the grey correlation theory and the particle swarm optimization algorithm (PSO). The simulation results showed that the energy consumption could reduced by 17.6% under the control of the multi–objective PSO as the temperature dropped from 31.5℃ to 24.51℃ and the humidity increased from 47.2% to 59.35%. Comparison with the linear weighted sum method and the single objective PSO, it could regulate the temperature and the humidity to meet the requirements for the growth of tea seedlings in greenhouse within 20 minutes under the condition of opening sun shading and spraying conditions.关键词
温室/模型/灰色粒子群算法/多目标优化Key words
greenhouse/model/grey particle swarmalgorithm/multi–objective optimization分类
信息技术与安全科学引用本文复制引用
张雪花,张武,李叶云,蔡芮莹,朱小倩..基于灰色粒子群算法的温室环境多目标优化控制[J].湖南农业大学学报（自然科学版）,2017,43(2):217-221,5.基金项目
农业部引进国际先进科学技术"948"项目(2015–Z44) （2015–Z44）
农业部农业物联网技术集成与应用重点实验室开放基金(2016KL05) （2016KL05）
安徽农业大学引进与稳定人才科研项目(wd2015–05) （wd2015–05）
