{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T13:22:17Z","timestamp":1777209737363,"version":"3.51.4"},"reference-count":55,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,8,25]],"date-time":"2025-08-25T00:00:00Z","timestamp":1756080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["615735285"],"award-info":[{"award-number":["615735285"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The swift growth of the low-altitude economic ecosystem and Unmanned Aerial Vehicle (UAV) swarm applications across diverse sectors presents significant challenges for engineering managers in terms of effective performance evaluation and operational optimization. Traditional evaluation methods often struggle with the inherent complexities, dynamic nature, and multi-faceted performance criteria of UAV swarms. This study introduces a novel Bayesian Network (BN)-based multicriteria decision-making framework that systematically integrates expert intuition with real-time data. By employing variance decomposition, the framework establishes theoretically grounded, bidirectional mapping between expert-assigned weights and the network\u2019s probabilistic parameters, creating a unified model of subjective expertise and objective data. Comprehensive validation demonstrates the framework\u2019s efficacy in identifying critical performance drivers, including environmental awareness, communication ability, and a collaborative decision. Ultimately, our work provides engineering managers with a transparent and adaptive tool, offering actionable insights to inform resource allocation, guide technology adoption, and enhance the overall operational effectiveness of complex UAV swarm systems.<\/jats:p>","DOI":"10.3390\/e27090897","type":"journal-article","created":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T06:26:31Z","timestamp":1756189591000},"page":"897","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Bridging Intuition and Data: A Unified Bayesian Framework for Optimizing Unmanned Aerial Vehicle Swarm Performance"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3711-892X","authenticated-orcid":false,"given":"Ruiguo","family":"Zhong","sequence":"first","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710129, China"},{"name":"Thrust of Intelligent Transportation, Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511455, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zidong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"Xi\u2019an Electronic Engineering Research Institute, Xi\u2019an 710100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanghui","family":"Jin","sequence":"additional","affiliation":[{"name":"Nanjing Research Institute of Electronic Technology, Nanjing 210039, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2401-614X","authenticated-orcid":false,"given":"Shuangxia","family":"Bai","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoguang","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710129, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3885","DOI":"10.1109\/TCE.2024.3355061","article-title":"AI-Enabled Trajectory Optimization of Logistics UAVs with Wind Impacts in Smart Cities","volume":"70","author":"Du","year":"2024","journal-title":"IEEE Trans. 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