{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:35:19Z","timestamp":1787027719096,"version":"build-2736575974"},"reference-count":34,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"key research projects within the military","award":["LJ20202A070636"],"award-info":[{"award-number":["LJ20202A070636"]}]},{"name":"key research projects within the military","award":["LJ20191A030128"],"award-info":[{"award-number":["LJ20191A030128"]}]},{"name":"key research projects within the military","award":["22YFYSHZ00040"],"award-info":[{"award-number":["22YFYSHZ00040"]}]},{"DOI":"10.13039\/501100019065","name":"Tianjin Science and Technology Plan project","doi-asserted-by":"publisher","award":["LJ20202A070636"],"award-info":[{"award-number":["LJ20202A070636"]}],"id":[{"id":"10.13039\/501100019065","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019065","name":"Tianjin Science and Technology Plan project","doi-asserted-by":"publisher","award":["LJ20191A030128"],"award-info":[{"award-number":["LJ20191A030128"]}],"id":[{"id":"10.13039\/501100019065","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019065","name":"Tianjin Science and Technology Plan project","doi-asserted-by":"publisher","award":["22YFYSHZ00040"],"award-info":[{"award-number":["22YFYSHZ00040"]}],"id":[{"id":"10.13039\/501100019065","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomimetics"],"abstract":"<jats:p>Traditional algorithms such as Dijkstra and APF rely on complete environmental information for path planning, which results in numerous constraints during modeling. This not only increases the complexity of the algorithms but also reduces the efficiency and reliability of the planning. Swarm intelligence algorithms possess strong data processing and search capabilities, enabling them to efficiently solve path planning problems in different environments and generate approximately optimal paths. However, swarm intelligence algorithms suffer from issues like premature convergence and a tendency to fall into local optima during the search process. Thus, an improved Artificial Bee Colony-Beetle Antennae Search (IABCBAS) algorithm is proposed. Firstly, Tent chaos and non-uniform variation are introduced into the bee algorithm to enhance population diversity and spatial searchability. Secondly, the stochastic reverse learning mechanism and greedy strategy are incorporated into the beetle antennae search algorithm to improve direction-finding ability and the capacity to escape local optima, respectively. Finally, the weights of the two algorithms are adaptively adjusted to balance global search and local refinement. Results of experiments using nine benchmark functions and four comparative algorithms show that the improved algorithm exhibits superior path point search performance and high stability in both high- and low-dimensional environments, as well as in unimodal and multimodal environments. Ablation experiment results indicate that the optimization strategies introduced in the algorithm effectively improve convergence accuracy and speed during path planning. Results of the path planning experiments show that compared with the comparison algorithms, the average path planning distance of the improved algorithm is reduced by 23.83% in the 2D multi-obstacle environment, and the average planning time is shortened by 27.97% in the 3D surface environment. The improvement in path planning efficiency makes this algorithm of certain value in engineering applications.<\/jats:p>","DOI":"10.3390\/biomimetics10080503","type":"journal-article","created":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T07:49:58Z","timestamp":1754380198000},"page":"503","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Research on Path Planning Method for Mobile Platforms Based on Hybrid Swarm Intelligence Algorithms in Multi-Dimensional Environments"],"prefix":"10.3390","volume":"10","author":[{"given":"Shuai","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng 252000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Management and Economics, Tianjin University, Tianjin 300072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2727-0564","authenticated-orcid":false,"given":"Yuhong","family":"Du","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Mechatronics Equipment Technology, Tiangong University, Tianjn 300387, China"},{"name":"School of Mechanical Engineering, Tiangong University, Tianjin 300387, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Yang","sequence":"additional","affiliation":[{"name":"Analysis & Testing Center, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"120254","DOI":"10.1016\/j.eswa.2023.120254","article-title":"Path planning techniques for mobile robots: Review and prospect","volume":"227","author":"Liu","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.matcom.2021.10.032","article-title":"Golden eagle optimizer with double learning strategies for 3D path planning of UAV in power inspection","volume":"193","author":"Pan","year":"2022","journal-title":"Math. Comput. Simul."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"110050","DOI":"10.1016\/j.oceaneng.2021.110050","article-title":"AUV path planning for coverage search of static target in ocean environment","volume":"241","author":"Yao","year":"2021","journal-title":"Ocean Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105587","DOI":"10.1016\/j.cor.2021.105587","article-title":"A discrete optimisation approach for target path planning whilst evading sensors","volume":"138","author":"Beasley","year":"2022","journal-title":"Comput. Oper. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"109355","DOI":"10.1016\/j.oceaneng.2021.109355","article-title":"Path planning and obstacle avoidance for AUV: A review","volume":"235","author":"Cheng","year":"2021","journal-title":"Ocean Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1007\/s10846-021-01326-0","article-title":"DVG plus A* and RRT Path-Planners: A Comparison in a Highly Dynamic Environment","volume":"101","author":"Leonardo","year":"2021","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"110034","DOI":"10.1016\/j.knosys.2022.110034","article-title":"Forward search optimization and subgoal-based hybrid path planning to shorten and smooth global path for mobile robots","volume":"258","author":"Li","year":"2022","journal-title":"Knowledge-Based Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"114750","DOI":"10.1016\/j.oceaneng.2023.114750","article-title":"Global-local hierarchical path planning scheme for unmanned surface vehicles under dynamically unforeseen environments","volume":"280","author":"Zhao","year":"2023","journal-title":"Ocean Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Aljalaud, F., and Kurdi, H. (2023). Youcef-Toumi. Bio-Inspired Multi-UAV Path Planning Heuristics: A Review. Mathematics, 11.","DOI":"10.3390\/math11102356"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"108905","DOI":"10.1016\/j.cie.2022.108905","article-title":"ACO+PSO+A*: A bi-layer hybrid algorithm for multi-task path planning of an AUV","volume":"175","author":"Sui","year":"2023","journal-title":"Comput. Ind. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.cma.2018.04.037","article-title":"An adaptive multiscale approach for identifying multiple flaws based on XFEM and a discrete artificial fish swarm algorithm","volume":"339","author":"Zhao","year":"2018","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3051","DOI":"10.3390\/s23063051","article-title":"Bio-Inspired Optimization-Based Path Planning Algorithms in Unmanned Aerial Vehicles: A Survey","volume":"23","author":"Sabitri","year":"2023","journal-title":"Sensors"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chen, X., Tang, J., and Lao, S. (2020). Review of unmanned aerial vehicle swarm communication architectures and routing protocols. Appl. Sci., 10.","DOI":"10.3390\/app10103661"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2063","DOI":"10.18280\/ts.400524","article-title":"A novel swarm unmanned aerial vehicle system: Incorporating autonomous flight, real-time object detection, and coordinated intelligence for enhanced performance","volume":"40","author":"Bakirci","year":"2023","journal-title":"Trait. Signal"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"107579","DOI":"10.1016\/j.engappai.2023.107579","article-title":"Multi-robot path planning using learning-based artificial bee colony algorithm","volume":"129","author":"Cui","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"121303","DOI":"10.1016\/j.eswa.2023.121303","article-title":"A Q-learning based multi-strategy integrated artificial bee colony algorithm with application in unmanned vehicle path planning","volume":"236","author":"Ni","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"110868","DOI":"10.1016\/j.asoc.2023.110868","article-title":"A mixing algorithm of ACO and ABC for solving path planning of mobile robot","volume":"148","author":"Li","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106375","DOI":"10.1016\/j.engappai.2023.106375","article-title":"A new approach based on hybrid ant colony optimization-artificial bee colony algorithm for multi-objective electric vehicle routing problems","volume":"123","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liang, Q., Zhou, H., Yin, Y., and Xiong, W. (2022). An improved beetle antennae search path planning algorithm for vehicles. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0274646"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"108730","DOI":"10.1016\/j.compeleceng.2023.108730","article-title":"A path planning algorithm for mobile robot based on water flow potential field method and beetle antennae search algorithm","volume":"109","author":"Yu","year":"2023","journal-title":"Comput. Electr. Eng."},{"key":"ref_21","first-page":"31","article-title":"Learning sparrow algorithm with non-uniform search for global optimization","volume":"14","author":"Chen","year":"2023","journal-title":"Int. J. Swarm Intell. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"24603","DOI":"10.1007\/s00521-023-08207-7","article-title":"A modified adaptive sparrow search algorithm based on chaotic reverse learning and spiral search for global optimization","volume":"35","author":"Geng","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"110679","DOI":"10.1016\/j.knosys.2023.110679","article-title":"Fast random opposition-based learning Golden Jackal Optimization algorithm","volume":"275","author":"Sarada","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"116664","DOI":"10.1016\/j.cma.2023.116664","article-title":"MNEARO: A meta swarm intelligence optimization algorithm for engineering applications","volume":"419","author":"Hu","year":"2023","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"04023019","DOI":"10.1061\/AJRUA6.RUENG-1015","article-title":"No-Free-Lunch Theorems for Reliability Analysis","volume":"9","author":"Mohsen","year":"2023","journal-title":"Asce-Asme J. Risk Uncertain. Eng. Syst. Part A-Civ. Eng."},{"key":"ref_26","first-page":"2508414","article-title":"Tent Chaotic Map and Population Classification Evolution Strategy-Based Dragonfly Algorithm for Global Optimization","volume":"2022","author":"Wang","year":"2022","journal-title":"Math. Probl. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"115124","DOI":"10.1016\/j.oceaneng.2023.115124","article-title":"Multi-objective structural profile optimization of ships based on improved Artificial Bee Colony algorithm and structural component library","volume":"283","author":"Jiang","year":"2023","journal-title":"Ocean Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"116158","DOI":"10.1016\/j.eswa.2021.116158","article-title":"Reptile search algorithm(RSA):A nature-inspired meta-heuristic optimizer","volume":"191","author":"Laith","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Xiao, W., Li, G., Liu, C., and Tan, L. (2023). A novel chaotic and neighborhood search-based artificial bee colony algorithm for solving optimization problems. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-44770-8"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, H., Liang, Y., and Meng, X. (2023). A UAV Path Planning Method for Building Surface Information Acquisition Utilizing Opposition-Based Learning Artificial Bee Colony Algorithm. Remote Sens., 15.","DOI":"10.3390\/rs15174312"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2443","DOI":"10.1007\/s42235-023-00389-z","article-title":"The Application of Hybrid Krill Herd Artificial Hummingbird Algorithm for Scientific Workflow Scheduling in Fog Computing","volume":"20","author":"Daniel","year":"2023","journal-title":"J. Bionic Eng."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, T., Li, Z., Wu, Y., and Miao, H. (2022). Software Defects Prediction Based on Hybrid Beetle Antennae Search Algorithm and Artificial Bee Colony Algorithm with Comparison. Axioms, 11.","DOI":"10.3390\/axioms11070305"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Dong, E., Chen, Z., Wang, R., and Zhao, J. (2022). Optimization of Two-Dimensional Extended Warranty Scheme for Failure Dependence of a Multi-Component System with Improved PSO-BAS Algorithm. Processes, 10.","DOI":"10.3390\/pr10081479"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ni, J., Tang, J., and Wang, R. (2022). Hybrid Algorithm of Improved Beetle Antenna Search and Artificial Fish Swarm. Appl. Sci., 12.","DOI":"10.3390\/app122413044"}],"container-title":["Biomimetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-7673\/10\/8\/503\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:21:12Z","timestamp":1760034072000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-7673\/10\/8\/503"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,1]]},"references-count":34,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["biomimetics10080503"],"URL":"https:\/\/doi.org\/10.3390\/biomimetics10080503","relation":{},"ISSN":["2313-7673"],"issn-type":[{"value":"2313-7673","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,1]]}}}