Min Liu , Yundong Yuan , Aobo Xu , Tianhu Deng , Ling Jian
{"title":"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","authors":"Min Liu , Yundong Yuan , Aobo Xu , Tianhu Deng , Ling Jian","doi":"10.1016/j.ins.2024.121593","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"690 ","pages":"Article 121593"},"PeriodicalIF":8.1000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A learning-based artificial bee colony algorithm for operation optimization in gas pipelines\",\"authors\":\"Min Liu , Yundong Yuan , Aobo Xu , Tianhu Deng , Ling Jian\",\"doi\":\"10.1016/j.ins.2024.121593\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":51063,\"journal\":{\"name\":\"Information Sciences\",\"volume\":\"690 \",\"pages\":\"Article 121593\"},\"PeriodicalIF\":8.1000,\"publicationDate\":\"2024-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Sciences\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S002002552401507X\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"0\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Sciences","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S002002552401507X","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
A learning-based artificial bee colony algorithm for operation optimization in gas pipelines
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.
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.