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A dynamic multi-objective optimization model with interactivity and uncertainty for real-time reservoir flood control operation

机译:具有交互性和不确定性的动态多目标优化模型,用于水库实时防洪调度

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Despite the successes of both multi-objective optimization and uncertainty handling techniques in reservoir flood control operation, no work has been done yet on developing and investigating dynamic multi-objective optimization models for this problem. In this work, a dynamic multi-objective optimization model with interactivity and uncertainty was developed for the real-time reservoir flood control operation. Accordingly, a dynamic multi-objective optimization algorithmic framework with two newly designed change reaction strategies was proposed for solving the proposed dynamic model. Following the proposed algorithmic framework, any evolutionary multi-objective optimization algorithm can be converted into a dynamic optimizer. After investigating the difficulty variation of the proposed dynamic model, the effectiveness and robustness of the proposed algorithmic framework have been validated based on experiential studies on two typical floods of Ankang reservoir. (C) 2019 Published by Elsevier Inc.
机译:尽管在水库防洪调度中多目标优化和不确定性处理技术都取得了成功,但尚未针对此问题开发和研究动态多目标优化模型的工作。在这项工作中,开发了具有交互性和不确定性的动态多目标优化模型,用于实时水库防洪调度。因此,提出了一种动态的多目标优化算法框架,该框架具有两种新设计的变化反应策略,用于解决所提出的动力学模型。按照提出的算法框架,任何进化的多目标优化算法都可以转换为动态优化器。在研究了所提出的动态模型的难度变化之后,基于对安康水库两次典型洪水的经验研究,验证了所提出算法框架的有效性和鲁棒性。 (C)2019由Elsevier Inc.发布

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