针对非原点最优的复杂优化问题(最优解不在坐标原点),提出了一种基于随机交叉-自学策略的教与学优化算法(teaching and learning optimization algorithm based on random crossover-self-study strategy,CSTLBO)。对标准教与学优化算...针对非原点最优的复杂优化问题(最优解不在坐标原点),提出了一种基于随机交叉-自学策略的教与学优化算法(teaching and learning optimization algorithm based on random crossover-self-study strategy,CSTLBO)。对标准教与学优化算法的“教阶段”和“学阶段”的空间扰动进行了几何解释,改进了原有的“教阶段”和“学阶段”,并引入随机交叉策略和“自学”策略来提高算法的全局寻优能力。通过使用20个Benchmark函数进行仿真,并与6种改进的教与学优化算法进行结果比较及Wilcoxon秩和检验分析,结果表明CSTLBO算法能有效避免陷入局部最优,具有良好的全局搜索能力,求解精度高,稳定性好。展开更多
The flowering forecast provides recommendations for orchard cleaning, pest control, field management and fertilization, which can help increase tree vigor and resistance. Flowering forecast is not only an important pa...The flowering forecast provides recommendations for orchard cleaning, pest control, field management and fertilization, which can help increase tree vigor and resistance. Flowering forecast is not only an important part of the construction of agro-meteorological index system, but also an important part of the meteorological service system. In this paper, by analyzing local meteorological data and phenological data of “Red Fuji” apples in Fen County, Linfen City, Shanxi Province, with the help of machine learning and neural networks, we proposed a method based on the combination of time series forecasting and classification forecasting is proposed to complete the dynamic forecasting model of local flowering in Ji County. Then, we evaluated the effectiveness of the model based on the number of error days and the number of days in advance. The implementation shows that the proposed multivariable LSTM network has a good effect on the prediction of meteorological factors. The model loss is less than 0.2. In the two-category task of flowering judgment, the idea of combining strategies in ensemble learning improves the effect of flowering judgment, and its AUC value increases from 0.81 and 0.80 of single model RF and AdaBoost to 0.82. The proposed model has high applicability and accuracy for flowering forecast. At the same time, the model solves the problem of rounding decimals in the prediction of flowering dates by the regression method.展开更多
文摘针对非原点最优的复杂优化问题(最优解不在坐标原点),提出了一种基于随机交叉-自学策略的教与学优化算法(teaching and learning optimization algorithm based on random crossover-self-study strategy,CSTLBO)。对标准教与学优化算法的“教阶段”和“学阶段”的空间扰动进行了几何解释,改进了原有的“教阶段”和“学阶段”,并引入随机交叉策略和“自学”策略来提高算法的全局寻优能力。通过使用20个Benchmark函数进行仿真,并与6种改进的教与学优化算法进行结果比较及Wilcoxon秩和检验分析,结果表明CSTLBO算法能有效避免陷入局部最优,具有良好的全局搜索能力,求解精度高,稳定性好。
文摘The flowering forecast provides recommendations for orchard cleaning, pest control, field management and fertilization, which can help increase tree vigor and resistance. Flowering forecast is not only an important part of the construction of agro-meteorological index system, but also an important part of the meteorological service system. In this paper, by analyzing local meteorological data and phenological data of “Red Fuji” apples in Fen County, Linfen City, Shanxi Province, with the help of machine learning and neural networks, we proposed a method based on the combination of time series forecasting and classification forecasting is proposed to complete the dynamic forecasting model of local flowering in Ji County. Then, we evaluated the effectiveness of the model based on the number of error days and the number of days in advance. The implementation shows that the proposed multivariable LSTM network has a good effect on the prediction of meteorological factors. The model loss is less than 0.2. In the two-category task of flowering judgment, the idea of combining strategies in ensemble learning improves the effect of flowering judgment, and its AUC value increases from 0.81 and 0.80 of single model RF and AdaBoost to 0.82. The proposed model has high applicability and accuracy for flowering forecast. At the same time, the model solves the problem of rounding decimals in the prediction of flowering dates by the regression method.
