{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T05:09:12Z","timestamp":1781154552964,"version":"3.54.1"},"reference-count":42,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T00:00:00Z","timestamp":1689552000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62062021"],"award-info":[{"award-number":["62062021"]}]},{"name":"National Natural Science Foundation of China","award":["61872034"],"award-info":[{"award-number":["61872034"]}]},{"name":"National Natural Science Foundation of China","award":["62166006"],"award-info":[{"award-number":["62166006"]}]},{"name":"National Natural Science Foundation of China","award":["[2020]1Y254"],"award-info":[{"award-number":["[2020]1Y254"]}]},{"name":"National Natural Science Foundation of China","award":["Guizhou Science Foundation-ZK [2021] General 335"],"award-info":[{"award-number":["Guizhou Science Foundation-ZK [2021] General 335"]}]},{"name":"Natural Science Foundation of Guizhou Province","award":["62062021"],"award-info":[{"award-number":["62062021"]}]},{"name":"Natural Science Foundation of Guizhou Province","award":["61872034"],"award-info":[{"award-number":["61872034"]}]},{"name":"Natural Science Foundation of Guizhou Province","award":["62166006"],"award-info":[{"award-number":["62166006"]}]},{"name":"Natural Science Foundation of Guizhou Province","award":["[2020]1Y254"],"award-info":[{"award-number":["[2020]1Y254"]}]},{"name":"Natural Science Foundation of Guizhou Province","award":["Guizhou Science Foundation-ZK [2021] General 335"],"award-info":[{"award-number":["Guizhou Science Foundation-ZK [2021] General 335"]}]},{"name":"Guizhou Provincial Science and Technology Projects","award":["62062021"],"award-info":[{"award-number":["62062021"]}]},{"name":"Guizhou Provincial Science and Technology Projects","award":["61872034"],"award-info":[{"award-number":["61872034"]}]},{"name":"Guizhou Provincial Science and Technology Projects","award":["62166006"],"award-info":[{"award-number":["62166006"]}]},{"name":"Guizhou Provincial Science and Technology Projects","award":["[2020]1Y254"],"award-info":[{"award-number":["[2020]1Y254"]}]},{"name":"Guizhou Provincial Science and Technology Projects","award":["Guizhou Science Foundation-ZK [2021] General 335"],"award-info":[{"award-number":["Guizhou Science Foundation-ZK [2021] General 335"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Path planning is a challenging, computationally complex optimization task in high-dimensional scenarios. The metaheuristic algorithm provides an excellent solution to this problem. The dung beetle optimizer (DBO) is a recently developed metaheuristic algorithm inspired by the biological behavior of dung beetles. However, it still has the drawbacks of poor global search ability and being prone to falling into local optima. This paper presents a multi-strategy enhanced dung beetle optimizer (MDBO) for the three-dimensional path planning of an unmanned aerial vehicle (UAV). First, we used the Beta distribution to dynamically generate reflection solutions to explore more search space and allow particles to jump out of the local optima. Second, the Levy distribution was introduced to handle out-of-bounds particles. Third, two different cross operators were used to improve the updating stage of thief beetles. This strategy accelerates convergence and balances exploration and development capabilities. Furthermore, the MDBO was proven to be effective by comparing seven state-of-the-art algorithms on 12 benchmark functions, the Wilcoxon rank sum test, and the CEC 2021 test suite. In addition, the time complexity of the algorithm was also analyzed. Finally, the performance of the MDBO in path planning was verified in the three-dimensional path planning of UAVs in oil and gas plants. In the most challenging task scenario, the MDBO successfully searched for feasible paths with the mean and standard deviation of the objective function as low as 97.3 and 32.8, which were reduced by 39.7 and 14, respectively, compared to the original DBO. The results demonstrate that the proposed MDBO had improved optimization accuracy and stability and could better find a safe and optimal path in most scenarios than the other metaheuristics.<\/jats:p>","DOI":"10.3390\/sym15071432","type":"journal-article","created":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T01:06:36Z","timestamp":1689555996000},"page":"1432","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["Multi-Strategy Enhanced Dung Beetle Optimizer and Its Application in Three-Dimensional UAV Path Planning"],"prefix":"10.3390","volume":"15","author":[{"given":"Qianwen","family":"Shen","sequence":"first","affiliation":[{"name":"School of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Damin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingshan","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"He","sequence":"additional","affiliation":[{"name":"School of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1049\/iet-rsn.2017.0251","article-title":"State-of-the-art technologies for UAV inspections","volume":"12","author":"Jordan","year":"2018","journal-title":"IET Radar Sonar Navig."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1109\/JIOT.2020.3012835","article-title":"AoI-Minimal Trajectory Planning and Data Collection in UAV-Assisted Wireless Powered IoT Networks","volume":"8","author":"Hu","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"106857","DOI":"10.1016\/j.asoc.2020.106857","article-title":"A knee-guided differential evolution algorithm for unmanned aerial vehicle path planning in disaster management","volume":"98","author":"Yu","year":"2020","journal-title":"Appl. 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