{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T06:30:48Z","timestamp":1775889048151,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T00:00:00Z","timestamp":1740528000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sichuan Science and Technology Program","award":["2024NSFSC0137"],"award-info":[{"award-number":["2024NSFSC0137"]}]},{"name":"Sichuan Science and Technology Program","award":["Y2023313"],"award-info":[{"award-number":["Y2023313"]}]},{"name":"Innovation Fund of Postgraduate, Sichuan University of Science and Engineering","award":["2024NSFSC0137"],"award-info":[{"award-number":["2024NSFSC0137"]}]},{"name":"Innovation Fund of Postgraduate, Sichuan University of Science and Engineering","award":["Y2023313"],"award-info":[{"award-number":["Y2023313"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The resolution of the unmanned aerial vehicle (UAV) path-planning problem frequently leverages optimization algorithms as a foundational approach. Among these, the recently proposed crayfish optimization algorithm (COA) has garnered significant attention as a promising and noteworthy alternative. Nevertheless, COA\u2019s search efficiency tends to diminish in the later stages of the optimization process, making it prone to premature convergence into local optima. To address this limitation, an improved COA (ICOA) is proposed. To enhance the quality of the initial individuals and ensure greater population diversity, the improved algorithm utilizes chaotic mapping in conjunction with a stochastic inverse learning strategy to generate the initial population. This modification aims to broaden the exploration scope into higher-quality search regions, enhancing the algorithm\u2019s resilience against local optima entrapment and significantly boosting its convergence effectiveness. Additionally, a nonlinear control parameter is incorporated to enhance the algorithm\u2019s adaptivity. Simultaneously, a Cauchy variation strategy is applied to the population\u2019s optimal individuals, strengthening the algorithm\u2019s ability to overcome stagnation. ICOA\u2019s performance is evaluated by employing the IEEE CEC2017 benchmark function for testing purposes. Comparison results reveal that ICOA outperforms other algorithms in terms of optimization efficacy, especially when applied to complex spatial configurations and real-world problem-solving scenarios. The proposed algorithm is ultimately employed in UAV path planning, with its performance tested across a range of terrain obstacle models. The findings confirm that ICOA excels in searching for paths that achieve safe obstacle avoidance and lower trajectory costs. Its search accuracy is notably superior to that of the comparative algorithms, underscoring its robustness and efficiency. ICOA ensures the balanced exploration and exploitation of the search space, which are particularly crucial for optimizing UAV path planning in environments with symmetrical and asymmetrical constraints.<\/jats:p>","DOI":"10.3390\/sym17030356","type":"journal-article","created":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T06:15:33Z","timestamp":1740550533000},"page":"356","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An Improved Crayfish Optimization Algorithm: Enhanced Search Efficiency and Application to UAV Path Planning"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5728-1945","authenticated-orcid":false,"given":"Qinyuan","family":"Huang","sequence":"first","affiliation":[{"name":"School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong 643000, China"},{"name":"Artificial Intelligence Key Laboratory of Sichuan Province, Zigong 643000, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4985-5955","authenticated-orcid":false,"given":"Yuqi","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong 643000, China"}]},{"given":"Chengyang","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong 643000, China"}]},{"given":"Chen","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong 643000, China"}]},{"given":"Xiuchen","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong 643000, China"}]},{"given":"Fei","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong 643000, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10867","DOI":"10.1109\/TMC.2024.3384405","article-title":"Energy-efficient trajectory optimization with wireless charging in UAV-assisted MEC based on multi-objective reinforcement learning","volume":"23","author":"Song","year":"2024","journal-title":"IEEE Trans. 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