A learning-based artificial bee colony algorithm for operation optimization in gas pipelines

IF 8.1 1区 计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS Information Sciences Pub Date : 2024-10-28 DOI:10.1016/j.ins.2024.121593
Min Liu , Yundong Yuan , Aobo Xu , Tianhu Deng , Ling Jian
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引用次数: 0

Abstract

The operation optimization of compressors is crucial for powering natural gas transportation and minimizing the gas consumption of the compressors themselves. In the literature, continuous control variables are typically discretized to cope with the curse of dimensionality by traditional dynamic programming methods and meta-heuristics, such as genetic algorithms and ant colony optimization. To provide a more accurate prediction, we developed a learning-based artificial bee colony (ABC) algorithm by integrating deep reinforcement learning. The merits of this innovation lie in two folds: 1) introduces function approximation to address challenges posed by the continuous state associated with gas consumption; and 2) improves the basic ABC's search capacity and reduces the risk of converging into local optima. Furthermore, the technology of multi-label classification is employed in the function approximation method to support the simultaneous optimal control of compressors in all stations, which can significantly enhance decision efficiency. Computational studies on real data demonstrate that the proposed method outperforms existing methods in the literature in terms of gas consumption.
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一种基于学习的人工蜂群算法,用于优化天然气管道的运行
压缩机的运行优化对于天然气运输和最大限度降低压缩机本身的耗气量至关重要。在文献中,连续控制变量通常被离散化,以通过传统的动态编程方法和元启发式方法(如遗传算法和蚁群优化)来应对维度诅咒。为了提供更精确的预测,我们通过整合深度强化学习,开发了基于学习的人工蜂群(ABC)算法。这一创新的优点体现在两个方面:1)引入函数近似,以应对与天然气消耗相关的连续状态所带来的挑战;2)提高了人工蜂群的基本搜索能力,降低了收敛到局部最优的风险。此外,函数逼近法还采用了多标签分类技术,支持所有站点压缩机的同步优化控制,从而显著提高决策效率。对真实数据的计算研究表明,所提出的方法在耗气量方面优于文献中的现有方法。
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来源期刊
Information Sciences
Information Sciences 工程技术-计算机:信息系统
CiteScore
14.00
自引率
17.30%
发文量
1322
审稿时长
10.4 months
期刊介绍: Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.
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