Yang Qin , Jingwei Guo , Peijuan Xu , Duo Li , Yonggang Wang
{"title":"不同轨道对准下列车节能速度线生成:余弦退火的量子进化算法","authors":"Yang Qin , Jingwei Guo , Peijuan Xu , Duo Li , Yonggang Wang","doi":"10.1016/j.swevo.2025.102027","DOIUrl":null,"url":null,"abstract":"<div><div>The optimization of train speed profiles is an effective method for reducing energy consumption and operating costs for urban rail transit (URT). In recent years, numerous intelligent optimization algorithms have been employed to generate high-quality solutions for speed profile optimization models. However, most previous studies preset the train operation sequence, overlooking the complex impacts of track alignment diversity on train operation modes. This limitation hinders the applicability of these findings across multiple inter-stations within the URT network. Moreover, there has been insufficient validation of the proposed optimization algorithms under diverse train operation scenarios. This study reformulates the generation of train speed profiles as a multi-stage decision-making problem within a continuous speed space. By applying the law of conservation of energy, a dynamic model of the train operation process is established and the decision evolution of the train speed is mapped into a high-dimensional quantum space. Subsequently, an improved quantum evolutionary algorithm with adaptive rotation strategies is designed using the cosine annealing function (CA-QEA). Furthermore, a conditional quantum collapse mechanism is introduced to enhance the global search capability of the algorithm. Utilizing actual URT data from Tianjin, China, three test scenarios featuring various track alignments, including gentle slopes, energy-saving slopes, and multiple continuous steep slopes, are constructed. The results demonstrate that the proposed method can effectively reduce the traction energy consumption (TEC) in three different track alignment. The Friedman non-parametric test results indicate that the performance of CA-QEA surpasses that of several mature intelligent optimization algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102027"},"PeriodicalIF":8.2000,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Train energy-efficient speed profile generation under various track alignments: a quantum evolutionary algorithm with cosine annealing\",\"authors\":\"Yang Qin , Jingwei Guo , Peijuan Xu , Duo Li , Yonggang Wang\",\"doi\":\"10.1016/j.swevo.2025.102027\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The optimization of train speed profiles is an effective method for reducing energy consumption and operating costs for urban rail transit (URT). In recent years, numerous intelligent optimization algorithms have been employed to generate high-quality solutions for speed profile optimization models. However, most previous studies preset the train operation sequence, overlooking the complex impacts of track alignment diversity on train operation modes. This limitation hinders the applicability of these findings across multiple inter-stations within the URT network. Moreover, there has been insufficient validation of the proposed optimization algorithms under diverse train operation scenarios. This study reformulates the generation of train speed profiles as a multi-stage decision-making problem within a continuous speed space. By applying the law of conservation of energy, a dynamic model of the train operation process is established and the decision evolution of the train speed is mapped into a high-dimensional quantum space. Subsequently, an improved quantum evolutionary algorithm with adaptive rotation strategies is designed using the cosine annealing function (CA-QEA). Furthermore, a conditional quantum collapse mechanism is introduced to enhance the global search capability of the algorithm. Utilizing actual URT data from Tianjin, China, three test scenarios featuring various track alignments, including gentle slopes, energy-saving slopes, and multiple continuous steep slopes, are constructed. The results demonstrate that the proposed method can effectively reduce the traction energy consumption (TEC) in three different track alignment. The Friedman non-parametric test results indicate that the performance of CA-QEA surpasses that of several mature intelligent optimization algorithms.</div></div>\",\"PeriodicalId\":48682,\"journal\":{\"name\":\"Swarm and Evolutionary Computation\",\"volume\":\"97 \",\"pages\":\"Article 102027\"},\"PeriodicalIF\":8.2000,\"publicationDate\":\"2025-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Swarm and Evolutionary Computation\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2210650225001853\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Swarm and Evolutionary Computation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210650225001853","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Train energy-efficient speed profile generation under various track alignments: a quantum evolutionary algorithm with cosine annealing
The optimization of train speed profiles is an effective method for reducing energy consumption and operating costs for urban rail transit (URT). In recent years, numerous intelligent optimization algorithms have been employed to generate high-quality solutions for speed profile optimization models. However, most previous studies preset the train operation sequence, overlooking the complex impacts of track alignment diversity on train operation modes. This limitation hinders the applicability of these findings across multiple inter-stations within the URT network. Moreover, there has been insufficient validation of the proposed optimization algorithms under diverse train operation scenarios. This study reformulates the generation of train speed profiles as a multi-stage decision-making problem within a continuous speed space. By applying the law of conservation of energy, a dynamic model of the train operation process is established and the decision evolution of the train speed is mapped into a high-dimensional quantum space. Subsequently, an improved quantum evolutionary algorithm with adaptive rotation strategies is designed using the cosine annealing function (CA-QEA). Furthermore, a conditional quantum collapse mechanism is introduced to enhance the global search capability of the algorithm. Utilizing actual URT data from Tianjin, China, three test scenarios featuring various track alignments, including gentle slopes, energy-saving slopes, and multiple continuous steep slopes, are constructed. The results demonstrate that the proposed method can effectively reduce the traction energy consumption (TEC) in three different track alignment. The Friedman non-parametric test results indicate that the performance of CA-QEA surpasses that of several mature intelligent optimization algorithms.
期刊介绍:
Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.