基于个体优势遗传算法的水稻生育期模型参数优化

Parameters Optimization of Rice Development Stages Model Based on Individual Advantages Genetic Algorithm

【目的】快速并准确估算作物生育期模型参数。【方法】本文提出了一种新的改进型遗传算法——个体优势遗传算法(individual advantages genetic algorithm,IAGA),并应用于水稻生育期模型参数估算。在遗传算法的基础上引入个体优势算子,并改进了变异算子及种群更新策略。以完全嵌入方式耦合RiceGrow和ORYZA2000水稻生育期模型,实现了模型参数的自动率定。利用汕优63等5个水稻品种在徐州、高要等地的多年田间试验资料,对IAGA算法的有效性进行对比试验。【结果】(1)试验验证结果的RMSE<3.05 d,NRMSE<3.19%,MDA<2.41 d,R2>0.9885,表明利用IAGA获得的模型参数准确性较高。(2)调参的实测数据量大小对调参结果影响不大。由3年数据增加到6年数据,试验拟合结果最大NRMSE值由2.58%增大到3.08%,增加了0.5%。选择隔年并包含全生育期天数最大值与最小值的调参数据,可以获得较准确的模型参数值。(3)IAGA与复合形混合演化算法、遗传模拟退火算法以及标准粒子群算法相比,可获得更准确的模型参数值。【结论】IAGA算法可以实现水稻生育期模型参数的自动率定,为作物生长模型参数的快速准确估算提供了一种有效新方法。

[Objective] Fast and accurate estimation of crop growth model parameters is the basis of the crop system simulation. [ Method] In this paper, a newly improved genetic algorithm, named individual advantage genetic algorithm （IAGA）, is proposed and applied to the field of the parameters evaluation of the rice development stages model. Firstly, the individual advantage operator was introduced into the genetic algorithm, thus improved the mutation operator and the update strategy of population. Secondly, two rice development stages models, RiceGrow and ORYZA2000, were coupled with IAGA in a way of total embedment, and realized automatic estimation of the parameters in the models. At last, a series of comparative experiments were carried out to verify the effectiveness of IAGA with multi-year field trial data of Shanyou63, and other four rice varieties in Xuzhou, Gaoyao, etc. [Result] The experimental verification results which cover RMSE〈3.05 d, NRMSE〈3.19%, MDA〈2.41 d, R2〉0.9877, indicated that the accuracy of the model parameters obtained by IAGA was pretty high. The amount of data used for the parameters estimation had little effect on the results. The maximum NRMSE of the fitting results increased from 2.58% to 3.08% when the amount of data used for the parameters estimation from three years to six years was changed. More accurate model parameters were obtained when we select the data of every other year, including the maximum and minimum value of the whole growth period. Compared with the shuffled complex evolution algorithm, genetic simulated annealing algorithm and standard particle swarm algorithm, IAGA could obtain more accurate model parameters. [ Conclusion ] The IAGA can achieve automatic determination of ricedevelopment stages model parameters, therefore it provides an effective and new method for estimating parameters for crop growth model quickly and accurately.