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To address these challenges, this paper proposes the direction guided dung beetle optimizer (DGDBO). Within a unified multi\u2010level directional regulation framework, an adaptive centripetal\u2013centrifugal search strategy, a triangular deflection walk, and a quadratic interpolation\u2010based refinement operator are integrated to systematically coordinate exploration, local optima escape, and fine\u2010grained exploitation across different optimization stages. Furthermore, a continuous collision detection model based on spatiotemporal line segments unifies inter\u2010UAV and dynamic obstacle avoidance within a single analytical framework, ensuring flight safety while maintaining solution space connectivity. Evaluations on the CEC2017 benchmark suite show that DGDBO ranks first overall via the Friedman test, achieving optimal results on 25 of 29 functions with an average ranking of 1.172, outperforming the second\u2010best algorithm by approximately 1.97 in average ranking. In four dynamic multi\u2010UAV simulation scenarios of increasing complexity, DGDBO achieves the lowest mean path cost and smallest standard deviation among all six compared algorithms. Compared with the other competitor algorithms, DGDBO reduces mean path cost by an average of 16.4% and improves convergence stability by an average of 64%, with all performance advantages statistically confirmed by the Wilcoxon rank\u2010sum test. These results demonstrate that DGDBO provides a robust and efficient optimization solution for cooperative multi\u2010UAV path planning under complex dynamic constraints.<\/jats:p>","DOI":"10.1002\/cpe.70767","type":"journal-article","created":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T00:56:02Z","timestamp":1779756962000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>A<\/scp>\n                    Direction Guided Dung Beetle Optimizer for Cooperative Multi\u2010\n                    <scp>UAV<\/scp>\n                    Path Planning in Dynamic Environments"],"prefix":"10.1002","volume":"38","author":[{"given":"Ziyi","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechanical and Automotive Engineering Anhui Polytechnic University  Wuhu Anhui China"},{"name":"Anhui Provincial Key Laboratory of Advanced Detection and Intelligent Perception  Wuhu Anhui 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