Current multi-objective optimization methods, traditionally rooted in static models, often neglect uncertainties and environmental interactions such as forecast accuracy and reservoir conditions. This study introduces a novel multi-objective operational optimization model aimed at dynamically controlling reservoir water levels in evolving flood season environments. The proposed model conducts a comprehensive analysis, quantification, and prediction of water level control dynamics during flood seasons by integrating strategies that encompass runoff forecast acquisition, dynamic risk assessment, and adaptive decision-making responses. To enhance the model’s effectiveness, this research proposes the Dynamic Multi-Objective Multi-Strategy Co-evolution (DMMC) algorithm. This algorithm incorporates several strategies, including memory-based individual optimal adaptation, dynamic updating of diverse individuals, collaborative updating based on forecast data, and static optimization techniques. These strategies enable real-time monitoring, identification, and efficient response to environmental fluctuations, thereby optimizing the sustainable utilization of water resources. Numerical experiments and engineering case studies validate the efficacy of the proposed method, demonstrating its capability to accurately capture environmental trends and promptly respond to evolving conditions. The simulations confirm the rationality and reliability of the model, presenting a novel approach for effectively managing dynamic water level control during flood seasons.

中文翻译：

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一种在不断变化的汛期环境中动态控制水库水位的多目标运行优化方法


当前的多目标优化方法传统上植根于静态模型，通常忽略不确定性和环境相互作用，例如预测准确性和储层条件。本研究引入了一种新的多目标运营优化模型，旨在在不断变化的洪水季节环境中动态控制水库水位。所提出的模型通过整合包括径流预测获取、动态风险评估和自适应决策响应的策略，对洪水季节的水位控制动态进行全面分析、量化和预测。为了提高模型的有效性，本研究提出了动态多目标多策略协同进化 （DMMC） 算法。该算法结合了多种策略，包括基于记忆的个体最优适应、不同个体的动态更新、基于预测数据的协作更新以及静态优化技术。这些策略能够实时监测、识别和有效响应环境波动，从而优化水资源的可持续利用。数值实验和工程案例研究验证了所提出的方法的有效性，证明了其准确捕捉环境趋势并迅速响应不断变化的条件的能力。模拟证实了该模型的合理性和可靠性，为在洪水季节有效管理动态水位控制提供了一种新方法。