Distribution Network Path Planning Method and System Based on Artificial Intelligence Optimization Algorithm

Distribution Network Path Planning Method and System Based on Artificial Intelligence Optimization Algorithm | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Distribution Network Path Planning Method and System Based on Artificial Intelligence Optimization Algorithm Shaoyan Jiang, Jiaxin Zheng, Lifeng Du, Shaoying Su, Hanming Tan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6365730/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract The foundation of dependable, safe, and economically viable power system operation is sensible transmission network design, which is essential given the ever-increasing size of power grids. Traditional mathematical optimization approaches have a hard time solving the transmission network route planning issue because it is large-scale, non-linear, and has high dimensions. In order to resolve the transmission network planning issue, this research employs the ant colony algorithm, a highly intelligent bionic optimization tool. Using it to seek the paths and lessen the coupling among parameters is suggested as an enhanced ant colony method. With the assumption of accurate convergence in mind, the ant colony method significantly improves the computation speed for transmission network design. The paper's modified ant colony algorithm outperforms the state-of-the-art in terms of processing time and efficiency in searching for the ideal transmission line route. A computerized model of the electrical system that includes power cables, nodes (such as transformers and substations), and the capacity, impedance, and position of each component. A basic topic with diverse applications, the route planning issue is a staple in many domains. Scholarly interest in finding a solution to the route optimization issue using deep reinforcement learning technologies has grown in recent years, making it a popular avenue for path planning problems. In this research, we will examine a power distribution optimization route approach, use deep reinforcement learning to address the continuous route planning issue, and do experiments in a Miniworld maze. A neural network representation of the reward function is used to suggest a reward shaping DDPG algorithm that optimizes the reward functionality dynamically. In a study that compared DDPG to genetic algorithms, Binary Swarm Optimization, while the historical average approach, it was found that the latter had a small and less than ideal accuracy rate, was easy to calculate, and showed little change in accuracy with increasing data. The genetic algorithm's accuracy hovers around 70%; it degrades with increasing training size. Eventually stabilizing at about 83%, the forecasting accuracy rate increased in tandem with the training system's expansion, leading to a deeper learning model with a higher training level. Distribution Network Path Planning Artificial Intelligence Optimization Algorithm Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 24 Jun, 2025 Reviewers agreed at journal 10 Jun, 2025 Reviewers agreed at journal 07 Jun, 2025 Reviews received at journal 05 Jun, 2025 Reviewers agreed at journal 05 Jun, 2025 Reviews received at journal 01 Jun, 2025 Reviews received at journal 27 May, 2025 Reviewers agreed at journal 21 May, 2025 Reviewers agreed at journal 21 May, 2025 Reviewers agreed at journal 20 May, 2025 Reviewers invited by journal 20 May, 2025 Editor assigned by journal 20 May, 2025 Submission checks completed at journal 20 May, 2025 First submitted to journal 20 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6365730","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":460163411,"identity":"e6133913-3cdb-49e6-ab72-3b377c713c86","order_by":0,"name":"Shaoyan 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Traditional mathematical optimization approaches have a hard time solving the transmission network route planning issue because it is large-scale, non-linear, and has high dimensions. In order to resolve the transmission network planning issue, this research employs the ant colony algorithm, a highly intelligent bionic optimization tool. Using it to seek the paths and lessen the coupling among parameters is suggested as an enhanced ant colony method. With the assumption of accurate convergence in mind, the ant colony method significantly improves the computation speed for transmission network design. The paper's modified ant colony algorithm outperforms the state-of-the-art in terms of processing time and efficiency in searching for the ideal transmission line route. A computerized model of the electrical system that includes power cables, nodes (such as transformers and substations), and the capacity, impedance, and position of each component. 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The genetic algorithm's accuracy hovers around 70%; it degrades with increasing training size. 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