{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T14:30:48Z","timestamp":1780065048687,"version":"3.54.0"},"reference-count":33,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T00:00:00Z","timestamp":1698105600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJICC"],"published-print":{"date-parts":[[2024,5,30]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Path planning is an important part of UAV mission planning. The main purpose of this paper is to overcome the shortcomings of the standard particle swarm optimization (PSO) such as easy to fall into the local optimum, so that the improved PSO applied to the UAV path planning can enable the UAV to plan a better quality path.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>Firstly, the adaptation function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself. Secondly, the standard PSO is improved, and the improved particle swarm optimization with multi-strategy fusion (MFIPSO) is proposed. The method introduces class sigmoid inertia weight, adaptively adjusts the learning factors and at the same time incorporates K-means clustering ideas and introduces the Cauchy perturbation factor. Finally, MFIPSO is applied to UAV path planning.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>Simulation experiments are conducted in simple and complex scenarios, respectively, and the quality of the path is measured by the fitness value and straight line rate, and the experimental results show that MFIPSO enables the UAV to plan a path with better quality.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>Aiming at the standard PSO is prone to problems such as premature convergence, MFIPSO is proposed, which introduces class sigmoid inertia weight and adaptively adjusts the learning factor, balancing the global search ability and local convergence ability of the algorithm. The idea of K-means clustering algorithm is also incorporated to reduce the complexity of the algorithm while maintaining the diversity of particle swarm. In addition, the Cauchy perturbation is used to avoid the algorithm from falling into local optimum. Finally, the adaptability function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself, which improves the accuracy of the evaluation model.<\/jats:p><\/jats:sec>","DOI":"10.1108\/ijicc-06-2023-0140","type":"journal-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T04:10:47Z","timestamp":1697861447000},"page":"213-235","source":"Crossref","is-referenced-by-count":20,"title":["Improved particle swarm optimization based on multi-strategy fusion for UAV\u00a0path planning"],"prefix":"10.1108","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5577-5940","authenticated-orcid":false,"given":"Zijing","family":"Ye","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2301-8943","authenticated-orcid":false,"given":"Huan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0881-459X","authenticated-orcid":false,"given":"Wenhong","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2023,10,24]]},"reference":[{"key":"key2024061211535943200_ref001","first-page":"260","article-title":"IoD swarms collision avoidance via improved particle swarm optimization","volume":"142","year":"2020","journal-title":"Transportation Research Part A: Policy and Practice"},{"issue":"2","key":"key2024061211535943200_ref002","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1017\/S0373463321000825","article-title":"A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture","volume":"75","year":"2022","journal-title":"The Journal of Navigation"},{"key":"key2024061211535943200_ref003","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1007\/s11182-021-02403-5","article-title":"A comprehensively improved particle swarm optimization algorithm to guarantee particle activity","volume":"64","year":"2021","journal-title":"Russian Physics Journal"},{"issue":"06","key":"key2024061211535943200_ref004","first-page":"1","article-title":"Three-dimensional path planning based on fuzzy logic particle swarm algorithm","volume":"27","year":"2020","journal-title":"Electro-Optics and Control"},{"issue":"18","key":"key2024061211535943200_ref005","doi-asserted-by":"crossref","first-page":"8977","DOI":"10.3390\/app12188977","article-title":"Chaos particle swarm optimization enhancement algorithm for UAV safe path planning","volume":"12","year":"2022","journal-title":"Applied Sciences"},{"issue":"5","key":"key2024061211535943200_ref006","doi-asserted-by":"crossref","first-page":"3403","DOI":"10.1016\/j.aej.2021.08.058","article-title":"A novel ant colony algorithm for solving shortest path problems with fuzzy arc weights","volume":"61","year":"2022","journal-title":"Alexandria Engineering Journal"},{"key":"key2024061211535943200_ref008","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1007\/s40747-021-00278-0","article-title":"Modified artificial bee colony algorithm for solving mixed interval-valued fuzzy shortest path problem","volume":"7","year":"2021","journal-title":"Complex and Intelligent Systems"},{"issue":"12","key":"key2024061211535943200_ref009","doi-asserted-by":"crossref","first-page":"5791","DOI":"10.3390\/app12125791","article-title":"Modified A-Star (A*) approach to plan the motion of a quadrotor UAV in three-dimensional obstacle-cluttered environment","volume":"12","year":"2022","journal-title":"Applied Sciences"},{"issue":"3","key":"key2024061211535943200_ref010","first-page":"86","article-title":"Three-dimensional path planning based on improved PSO algorithm","volume":"28","year":"2021","journal-title":"Electronics Optics and Control"},{"key":"key2024061211535943200_ref011","doi-asserted-by":"crossref","first-page":"7350","DOI":"10.1007\/s10489-020-02082-8","article-title":"A novel hybrid particle swarm optimization for multi-UAV cooperate path planning","volume":"51","year":"2021","journal-title":"Applied Intelligence"},{"issue":"6","key":"key2024061211535943200_ref012","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.23919\/JSEE.2021.000124","article-title":"Self-organized search-attack mission planning for UAV swarm based on wolf pack hunting behavior","volume":"32","year":"2021","journal-title":"Journal of Systems Engineering and Electronics"},{"key":"key2024061211535943200_ref013","first-page":"1","article-title":"A novel three-dimensional path planning method for fixed-wing UAV using improved particle swarm optimization algorithm","volume":"2021","year":"2021","journal-title":"International Journal of Aerospace Engineering"},{"issue":"7","key":"key2024061211535943200_ref014","doi-asserted-by":"crossref","first-page":"1968","DOI":"10.3390\/en14071968","article-title":"Path planning for a solar-powered UAV inspecting mountain sites for safety and rescue","volume":"14","year":"2021","journal-title":"Energies"},{"issue":"2","key":"key2024061211535943200_ref015","article-title":"Implementation of UAV smooth path planning by improved parallel genetic algorithm on controller area network","volume":"35","year":"2022","journal-title":"Journal of Aerospace Engineering"},{"key":"key2024061211535943200_ref016","doi-asserted-by":"crossref","first-page":"192760","DOI":"10.1109\/ACCESS.2020.3032929","article-title":"A dynamic artificial potential field (D-APF) UAV path planning technique for following ground moving targets","volume":"8","year":"2020","journal-title":"IEEE Access"},{"issue":"2","key":"key2024061211535943200_ref017","doi-asserted-by":"crossref","first-page":"92","DOI":"10.3390\/drones7020092","article-title":"An improved probabilistic roadmap planning method for safe indoor flights of unmanned aerial vehicles","volume":"7","year":"2023","journal-title":"Drones"},{"key":"key2024061211535943200_ref018","first-page":"1942","article-title":"Particle swarm optimization","year":"1995"},{"key":"key2024061211535943200_ref019","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.ast.2016.08.017","article-title":"Adaptive sensitivity decision based path planning algorithm for unmanned aerial vehicle with improved particle swarm optimization","volume":"58","year":"2016","journal-title":"Aerospace Science and Technology"},{"key":"key2024061211535943200_ref020","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s10489-018-1258-3","article-title":"Enhanced particle swarm optimization with multi-swarm and multi-velocity for optimizing high-dimensional problems","volume":"49","year":"2019","journal-title":"Applied Intelligence"},{"issue":"107376","key":"key2024061211535943200_ref007","first-page":"1","article-title":"Safety-enhanced UAV path planning with spherical vector-based particle swarm optimization","volume":"107","year":"2021","journal-title":"Applied Soft Computing"},{"issue":"12","key":"key2024061211535943200_ref021","doi-asserted-by":"crossref","first-page":"14101","DOI":"10.1007\/s10489-022-03254-4","article-title":"UAV swarm path planning with reinforcement learning for field prospecting","volume":"52","year":"2022","journal-title":"Applied Intelligence"},{"key":"key2024061211535943200_ref022","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.isatra.2019.08.018","article-title":"Efficient path planning for UAV formation via comprehensively improved particle swarm optimization","volume":"97","year":"2020","journal-title":"ISA Transactions"},{"key":"key2024061211535943200_ref023","first-page":"1","article-title":"Mission planning for unmanned aerial vehicles based on Voronoi diagram-Tabu genetic algorithm","volume":"2021","year":"2021","journal-title":"Wireless Communications and Mobile Computing"},{"key":"key2024061211535943200_ref024","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.swevo.2018.01.011","article-title":"MPSO: modified particle swarm optimization and its applications","volume":"41","year":"2018","journal-title":"Swarm and Evolutionary Computation"},{"issue":"09","key":"key2024061211535943200_ref025","first-page":"1690","article-title":"UAV path planning based on improved particle swarm optimization","volume":"42","year":"2020","journal-title":"Computer Engineering and Science"},{"key":"key2024061211535943200_ref026","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.psep.2022.02.011","article-title":"An intelligent UAV path planning optimization method for monitoring the risk of unattended offshore oil platforms","volume":"160","year":"2022","journal-title":"Process Safety and Environmental Protection"},{"key":"key2024061211535943200_ref027","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.swevo.2018.04.006","article-title":"A fitness-based multi-role particle swarm optimization","volume":"44","year":"2019","journal-title":"Swarm and Evolutionary Computation"},{"key":"key2024061211535943200_ref028","first-page":"1","article-title":"Subpopulation particle swarm optimization with a hybrid mutation strategy","volume":"2022","year":"2022","journal-title":"Computational Intelligence and Neuroscience"},{"key":"key2024061211535943200_ref029","first-page":"1","article-title":"Obstacle avoidance path planning for UAV based on improved RRT algorithm","volume":"2022","year":"2022","journal-title":"Discrete Dynamics in Nature and Society"},{"key":"key2024061211535943200_ref030","article-title":"A constrained differential evolution algorithm to solve UAV path planning in disaster scenarios","volume":"204","year":"2020","journal-title":"Knowledge-Based Systems"},{"issue":"6","key":"key2024061211535943200_ref031","first-page":"22","article-title":"Path planning for logistics unmanned aerial vehicle in urban area","volume":"20","year":"2020","journal-title":"Journal of Transportation Systems Engineering and Information Technology"},{"issue":"4","key":"key2024061211535943200_ref032","doi-asserted-by":"crossref","first-page":"225","DOI":"10.3390\/biomimetics7040225","article-title":"Path planning with time windows for multiple UAVs based on gray wolf algorithm","volume":"7","year":"2022","journal-title":"Biomimetics"},{"issue":"01","key":"key2024061211535943200_ref033","first-page":"40","article-title":"Quantum particle swarm optimization algorithm of three-dimensional path planning of unmanned aerial vehicle","volume":"39","year":"2021","journal-title":"Aerospace Control"}],"container-title":["International Journal of Intelligent Computing and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJICC-06-2023-0140\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJICC-06-2023-0140\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:54:27Z","timestamp":1753397667000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/ijicc\/article\/17\/2\/213-235\/1232962"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,24]]},"references-count":33,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,10,24]]},"published-print":{"date-parts":[[2024,5,30]]}},"alternative-id":["10.1108\/IJICC-06-2023-0140"],"URL":"https:\/\/doi.org\/10.1108\/ijicc-06-2023-0140","relation":{},"ISSN":["1756-378X"],"issn-type":[{"value":"1756-378X","type":"print"}],"subject":[],"published":{"date-parts":[[2023,10,24]]}}}