In this paper, we propose a multi-mutation optimization algorithm, Differential Evolution with Multi-Mutation Operator-Guided Communication (DE-MMOGC), implemented to improve the performance and convergence abilities of standard differential evolution in uncertain environments. DE-MMOGC introduces a communication-guided scheme integrated with multiple mutation operators to encourage exploration and avoid premature convergence. Along with this, it includes a dynamic operator selection mechanism to use the best-performing operator over successive generations. To assimilate real-world uncertainties and missing observations into the predictive model, the proposed algorithm is combined with the Ensemble Kalman Filter. To evaluate the efficacy of the proposed DE-MMOGC in uncertain systems, the unified framework is applied to improve the predictive accuracy of crop simulation models. These simulation models are essential to precision agriculture, as they make it easier to estimate crop growth in a variety of unpredictable weather scenarios. Additionally, precisely calibrating these models raises a challenge due to missing observations. Hence, the simplified WOFOST crop simulation model is incorporated in this study for leaf area index (LAI)-based crop yield estimation. DE-MMOGC enhances the WOFOST performance by optimizing crucial weather parameters (temperature and rainfall), since these parameters are highly uncertain across different crop varieties, such as wheat, rice, and cotton. The experimental study shows that DE-MMOGC outperforms the traditional evolutionary optimizers and achieves better correlation with real LAI values. We found that DE-MMOGC is a resilient solution for crop monitoring.

翻译：本文提出了一种多变异优化算法——多变异算子引导通信差分进化算法（DE-MMOGC），旨在提升标准差分进化算法在不确定环境下的性能与收敛能力。DE-MMOGC引入了一种与多种变异算子相结合的通信引导机制，以促进算法探索并避免早熟收敛。同时，该算法包含一种动态算子选择机制，能够在连续迭代中选用表现最优的变异算子。为将实际环境中的不确定性与缺失观测数据同化到预测模型中，所提算法与集合卡尔曼滤波相结合。为评估DE-MMOGC在不确定系统中的有效性，该统一框架被应用于提升作物模拟模型的预测精度。这些模拟模型对精准农业至关重要，因为它们能够帮助估算作物在各种不可预测天气情景下的生长状况。此外，由于观测数据缺失，对这些模型进行精确校准仍面临挑战。因此，本研究采用简化的WOFOST作物模拟模型进行基于叶面积指数（LAI）的作物产量估算。DE-MMOGC通过优化关键气象参数（温度和降雨量）来提升WOFOST模型的性能，因为这些参数在不同作物品种（如小麦、水稻和棉花）间存在高度不确定性。实验研究表明，DE-MMOGC优于传统进化优化器，并与实际LAI值具有更好的相关性。我们发现DE-MMOGC是一种稳健的作物监测解决方案。