{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T20:06:15Z","timestamp":1778789175077,"version":"3.51.4"},"reference-count":26,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100010002","name":"Ministry of Education","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC 113-2218-E-002-048"],"award-info":[{"award-number":["NSTC 113-2218-E-002-048"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC 114-2218-E-006-016"],"award-info":[{"award-number":["NSTC 114-2218-E-006-016"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC 113-2622-E-194-010"],"award-info":[{"award-number":["NSTC 113-2622-E-194-010"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC 113-2622-E-194-002"],"award-info":[{"award-number":["NSTC 113-2622-E-194-002"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NCTC 113-2218-E-006-021"],"award-info":[{"award-number":["NCTC 113-2218-E-006-021"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC 114-2218-E-002-029"],"award-info":[{"award-number":["NSTC 114-2218-E-002-029"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC 114-2622-E-194-004"],"award-info":[{"award-number":["NSTC 114-2622-E-194-004"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC 113-2224-E-194-001"],"award-info":[{"award-number":["NSTC 113-2224-E-194-001"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC 114-2224-E-194-001"],"award-info":[{"award-number":["NSTC 114-2224-E-194-001"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["114-2224-E-006-005"],"award-info":[{"award-number":["114-2224-E-006-005"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["114-2221-E-006-158-MY3"],"award-info":[{"award-number":["114-2221-E-006-158-MY3"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["114-2221-E-006-210-MY3"],"award-info":[{"award-number":["114-2221-E-006-210-MY3"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1016\/j.engappai.2025.111862","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T23:07:43Z","timestamp":1754348863000},"page":"111862","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":5,"special_numbering":"PA","title":["Deep reinforcement learning\u2013based collision avoidance strategy for multiple unmanned aerial vehicles"],"prefix":"10.1016","volume":"160","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5125-4420","authenticated-orcid":false,"given":"Ping-Huan","family":"Kuo","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0491-5713","authenticated-orcid":false,"given":"Kuan-Lin","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7673-7715","authenticated-orcid":false,"given":"Yu-Sian","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Yu-Chih","family":"Chiu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4068-0632","authenticated-orcid":false,"given":"Chao-Chung","family":"Peng","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2025.111862_bib1","article-title":"Algorithms for hyper-parameter optimization","volume":"24","author":"Bergstra","year":"2011","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2025.111862_bib3","doi-asserted-by":"crossref","first-page":"39269","DOI":"10.1109\/ACCESS.2023.3266991","article-title":"Adaptive chaotic marine predators hill climbing algorithm for large-scale design optimizations","volume":"11","author":"Dehkordi","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.engappai.2025.111862_bib4","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1109\/TITS.2024.3512784","article-title":"Eliminating uncertainty of driver's social preferences for Lane change decision-making in realistic simulation environment","volume":"26","author":"Deng","year":"2025","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"10.1016\/j.engappai.2025.111862_bib5","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107004","article-title":"An imbalanced classification approach for establishment of cause-effect relationship between heart-failure and pulmonary embolism using deep reinforcement learning","volume":"126","author":"Firdous","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2025.111862_bib6","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107287","article-title":"Deep reinforcement learning based planning method in state space for lunar rovers","volume":"127","author":"Gao","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2025.111862_bib27","doi-asserted-by":"crossref","first-page":"107099","DOI":"10.1016\/j.engappai.2023.107099","article-title":"Deep deterministic policy gradient based multi-UAV control for moving convoy tracking","volume":"126","author":"Garg","year":"2023","journal-title":"Eng Appl Artif Intell"},{"key":"10.1016\/j.engappai.2025.111862_bib7","doi-asserted-by":"crossref","first-page":"4600","DOI":"10.1109\/TSMC.2021.3098451","article-title":"Proximal policy optimization with policy feedback","volume":"52","author":"Gu","year":"2022","journal-title":"IEEE Trans. Syst. Man. Cybern Syst."},{"key":"10.1016\/j.engappai.2025.111862_bib8","doi-asserted-by":"crossref","first-page":"159672","DOI":"10.1109\/ACCESS.2021.3131772","article-title":"Guided soft actor critic: a guided deep reinforcement learning approach for partially observable markov decision processes","volume":"9","author":"Haklidir","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.engappai.2025.111862_bib9","doi-asserted-by":"crossref","first-page":"1605","DOI":"10.1109\/TMAG.2006.892113","article-title":"An efficient multiobjective optimizer based on genetic algorithm and approximation techniques for electromagnetic design","volume":"43","author":"Ho","year":"2007","journal-title":"IEEE Trans. Magn."},{"key":"10.1016\/j.engappai.2025.111862_bib10","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106703","article-title":"Subtask-masked curriculum learning for reinforcement learning with application to UAV maneuver decision-making","volume":"125","author":"Hou","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2025.111862_bib11","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107537","article-title":"Real-time assessment of surface cracks in concrete structures using integrated deep neural networks with autonomous unmanned aerial vehicle","volume":"129","author":"Kim","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2025.111862_bib12","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.1109\/LCOMM.2023.3265214","article-title":"UAV trajectory optimization for spectrum cartography: a PPO approach","volume":"27","author":"Li","year":"2023","journal-title":"IEEE Commun. Lett."},{"key":"10.1016\/j.engappai.2025.111862_bib13","doi-asserted-by":"crossref","first-page":"2005","DOI":"10.1109\/LCOMM.2020.3001227","article-title":"The LSTM-based advantage actor-critic learning for resource management in network slicing with user mobility","volume":"24","author":"Li","year":"2020","journal-title":"IEEE Commun. Lett."},{"key":"10.1016\/j.engappai.2025.111862_bib14","doi-asserted-by":"crossref","first-page":"7897","DOI":"10.1109\/TWC.2022.3162749","article-title":"Path planning for cellular-connected UAV: a DRL solution with quantum-inspired experience replay","volume":"21","author":"Li","year":"2022","journal-title":"IEEE Trans. Wireless Commun."},{"key":"10.1016\/j.engappai.2025.111862_bib15","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1109\/TIV.2022.3213703","article-title":"DRL-UTPS: DRL-based trajectory planning for unmanned aerial vehicles for data collection in dynamic IoT network","volume":"8","author":"Liu","year":"2023","journal-title":"IEEE Trans. Intell. Veh."},{"key":"10.1016\/j.engappai.2025.111862_bib16","doi-asserted-by":"crossref","first-page":"150330","DOI":"10.1109\/ACCESS.2021.3125895","article-title":"Enhanced deep belief network based on ensemble learning and tree-structured of parzen estimators: an optimal photovoltaic power forecasting method","volume":"9","author":"Massaoudi","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.engappai.2025.111862_bib17","doi-asserted-by":"crossref","first-page":"22646","DOI":"10.1109\/ACCESS.2022.3152557","article-title":"Novel design of slim mould optimizer for the solution of optimal power flow problems incorporating intermittent sources: a case study of Algerian electricity grid","volume":"10","author":"Mouassa","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.engappai.2025.111862_bib18","doi-asserted-by":"crossref","first-page":"150268","DOI":"10.1109\/ACCESS.2024.3476473","article-title":"Analytic integral backstepping controller for quadrotor trajectory tracking","volume":"12","author":"Peng","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.engappai.2025.111862_bib19","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1109\/TAES.2023.3329797","article-title":"Fixed-wing unmanned aerial vehicle rotary engine anomaly detection via online digital twin methods","volume":"60","author":"Peng","year":"2024","journal-title":"IEEE Trans. Aero. Electron. Syst."},{"key":"10.1016\/j.engappai.2025.111862_bib20","doi-asserted-by":"crossref","DOI":"10.1016\/j.jocs.2023.101944","article-title":"Design of constrained dynamic path planning algorithms in large-scale 3D point cloud maps for UAVs","volume":"67","author":"Peng","year":"2023","journal-title":"J. Comput. Sci."},{"key":"10.1016\/j.engappai.2025.111862_bib21","doi-asserted-by":"crossref","first-page":"3566","DOI":"10.1109\/TSMC.2022.3228901","article-title":"Deep reinforcement learning with a stage incentive mechanism of dense reward for robotic trajectory planning","volume":"53","author":"Peng","year":"2023","journal-title":"IEEE Trans. Syst. Man. Cybern Syst."},{"key":"10.1016\/j.engappai.2025.111862_bib22","author":"Pybullet"},{"key":"10.1016\/j.engappai.2025.111862_bib23","doi-asserted-by":"crossref","first-page":"11141","DOI":"10.1109\/JIOT.2021.3127873","article-title":"Deep reinforcement learning for flocking motion of Multi-UAV systems: learn from a digital twin","volume":"9","author":"Shen","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.engappai.2025.111862_bib24","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107728","article-title":"Adversarial deep reinforcement learning based robust depth tracking control for underactuated autonomous underwater vehicle","volume":"130","author":"Wang","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2025.111862_bib26","doi-asserted-by":"crossref","first-page":"108294","DOI":"10.1016\/j.engappai.2024.108294","article-title":"Multi-degree-of-freedom unmanned aerial vehicle control combining a hybrid brain-computer interface and visual obstacle avoidance","volume":"133","author":"Xie","year":"2024","journal-title":"Eng Appl Artif Intell"},{"key":"10.1016\/j.engappai.2025.111862_bib25","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107513","article-title":"Deep learning-based object detection in maritime unmanned aerial vehicle imagery: review and experimental comparisons","volume":"128","author":"Zhao","year":"2024","journal-title":"Eng. Appl. Artif. Intell."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197625018640?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197625018640?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T16:19:18Z","timestamp":1778516358000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197625018640"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":26,"alternative-id":["S0952197625018640"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2025.111862","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2025,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Deep reinforcement learning\u2013based collision avoidance strategy for multiple unmanned aerial vehicles","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2025.111862","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"111862"}}