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首页> 外文期刊>Neural computing & applications >Performance analysis of artificial bee colony (ABC) algorithm in optimizing release policy of Aswan High Dam
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Performance analysis of artificial bee colony (ABC) algorithm in optimizing release policy of Aswan High Dam

机译:人工蜂群算法在优化阿斯旺大坝释放策略中的性能分析

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摘要

The paper presents a study on developing an effective reservoir operation policy by using artificial bee colony (ABC) algorithm. The decision maker of a reservoir system always needs a guideline to operate the reservoir in an optimal way. Such guidelines named 'release curves' have developed for high-, medium-, and low-inflow category that can answer how much water needs to be released for a month by observing the reservoir level (storage condition). The Aswan High Dam of Egypt has been considered for the case study. For comparing the model efficiency, another heuristic approach - genetic algorithm (GA) - has been used. So far, GA is well established and most popular in reservoir release optimization. Historical inflow data for 18 years have been used for simulation purpose, and the general system performance-measuring indices (such as reliability, resiliency, and vulnerability) have been measured. The application procedure and problem formulation of ABC are very simple and can be used in optimizing reservoir system. After using the actual historical inflow, the release policy succeeded in meeting demand for about 98% of the total time period. According to the simulation results, ABC algorithm showed better performance than the GA approach in reservoir release optimization.
机译:本文提出了利用人工蜂群(ABC)算法制定有效水库调度策略的研究。水库系统的决策者始终需要一条准则来以最佳方式操作水库。已经针对高流量,中流量和低流量类别开发了这种名为“释放曲线”的准则,该准则可以通过观察水库水位(储存条件)来回答一个月需要释放多少水。案例研究考虑了埃及的阿斯旺高坝。为了比较模型效率,已使用了另一种启发式方法-遗传算法(GA)。到目前为止,遗传算法已经建立完善,并且在油藏优化中最受欢迎。 18年的历史流入数据已用于仿真目的,并且已测量了总体系统性能测量指标(例如可靠性,弹性和脆弱性)。 ABC的应用程序和问题表述非常简单,可用于优化油藏系统。使用实际的历史流入量后,发布策略成功满足了整个时间段约98%的需求。根据仿真结果,在油藏优化中,ABC算法的性能优于遗传算法。

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