Reservoir operation management using a new hybrid algorithm of Invasive Weed Optimization and Cuckoo Search Algorithm

IF 2.1 4区 环境科学与生态学 Q2 ENGINEERING, CIVIL
M. Trivedi, R. Shrivastava
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引用次数: 1

Abstract

Water scarcity throughout the world has led to major difficulties and complexities in managing water demands. These challenges gravitate towards the development of efficient methods for optimal reservoir operation. The present study aims to introduce a hybrid approach which integrates Invasive Weed Optimization (IWO) and Cuckoo Search Algorithm (CSA), with an objective to minimize the deficits for Indira Sagar Reservoir (ISR), India. To prevail over the limitations of the Weed Optimization Algorithm (WOA) and CSA, a critical comparison has been made in the study. The hybrid approach has improved the performance by 5 and 9% as compared to WOA and CSA, respectively. For the reservoir system, the Cv for 10 random runs was computed to be 0.0303 using the hybrid model, whereas for WOA and CSA, Cv was 0.22034 and 0.30698, respectively. Based on the performance measuring indices, results revealed that the hybrid model is more reliable and sustainable with the minimum error between release and demand. In addition, results reveal that the deficits have been reduced by 62% on average for the considered study period using the hybrid approach. Therefore, the results show that the proposed hybrid model has considerable potential to be used as an optimizer for complex reservoir operation problems.
基于入侵杂草优化和布谷鸟搜索算法的水库调度管理
世界各地的水资源短缺给管理用水需求带来了重大困难和复杂性。这些挑战促使开发有效的方法来优化油藏作业。本研究旨在介绍一种结合入侵杂草优化(IWO)和布谷鸟搜索算法(CSA)的混合方法,以最大限度地减少印度Indira Sagar水库(ISR)的损失。为了克服杂草优化算法(WOA)和CSA算法的局限性,在研究中进行了关键的比较。与WOA和CSA相比,混合方法的性能分别提高了5%和9%。对于储层系统,使用混合模型计算出10次随机运行的Cv为0.0303,而WOA和CSA的Cv分别为0.22034和0.30698。基于性能度量指标,结果表明该混合模型具有更强的可靠性和可持续性,且释放与需求之间的误差最小。此外,结果显示,在考虑的研究期间,使用混合方法的赤字平均减少了62%。结果表明,该混合模型在复杂油藏运行问题的优化方面具有较大的应用潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.10
自引率
21.10%
发文量
0
审稿时长
20 weeks
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