{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T13:25:28Z","timestamp":1768829128005,"version":"3.49.0"},"reference-count":42,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,12,21]],"date-time":"2018-12-21T00:00:00Z","timestamp":1545350400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["No.BLX201605,No.2016ZCQ08"],"award-info":[{"award-number":["No.BLX201605,No.2016ZCQ08"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.61703047"],"award-info":[{"award-number":["No.61703047"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Path planning of unmanned aerial vehicles (UAVs) in threatening and adversarial areas is a constrained nonlinear optimal problem which takes a great amount of static and dynamic constraints into account. Quantum-behaved pigeon-inspired optimization (QPIO) has been widely applied to such nonlinear problems. However, conventional QPIO is suffering low global convergence speed and local optimum. In order to solve the above problems, an improved QPIO algorithm, adaptive operator QPIO, is proposed in this paper. Firstly, a new initialization process based on logistic mapping method is introduced to generate the initial population of the pigeon-swarm. After that, to improve the performance of the map and compass operation, the factor parameter will be adaptively updated in each iteration, which can balance the ability between global and local search. In the final landmark operation, the gradual decreasing pigeon population-updating strategy is introduced to prevent premature convergence and local optimum. Finally, the demonstration of the proposed algorithm on UAV path planning problem is presented, and the comparison result indicates that the performance of our algorithm is better than that of particle swarm optimization (PSO), pigeon-inspired optimization (PIO), and its variants, in terms of convergence and accuracy.<\/jats:p>","DOI":"10.3390\/a12010003","type":"journal-article","created":{"date-parts":[[2018,12,21]],"date-time":"2018-12-21T12:14:49Z","timestamp":1545394489000},"page":"3","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning"],"prefix":"10.3390","volume":"12","author":[{"given":"Chunhe","family":"Hu","sequence":"first","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junguo","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1109\/MRA.2009.932529","article-title":"Autonomous UAV path planning and estimation","volume":"61","author":"Tisdale","year":"2009","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Trotta, A., D\u2019Andreagiovanni, F., Felice, M., Natalizio, E., and Chowdhury, K. (2018, January 15\u201319). When UAVs Ride A Bus: Towards Energy-efficient City-scale Video Surveillance. Proceedings of the IEEE INFOCOM 2018\u2014IEEE Conference on Computer Communications, Honolulu, HI, USA.","DOI":"10.1109\/INFOCOM.2018.8485863"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5429","DOI":"10.1109\/TAC.2017.2694547","article-title":"Distributed Biased Min-Consensus with Applications to Shortest Path Planning","volume":"62","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Otto, A., Agatz, N., Cambell, J., Golden, B., and Pesch, E. (2018). Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey. Networks, 1\u201348.","DOI":"10.1002\/net.21818"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ho, Y.J., and Liu, J.S. (2010, January 22\u201326). Simulated annealing based algorithm for smooth robot path planning with different kinematic constraints. Proceedings of the 2010 ACM Symposium on Applied Computing (SAC), Sierre, Switzerland.","DOI":"10.1145\/1774088.1774361"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sheng, J., He, G., Guo, W., and Li, J.H. (2010, January 16\u201318). An Improved Artificial Potential Field Algorithm for Virtual Human Path Planning. Proceedings of the International Conference on Entertainment for Education Digital Techniques & Systems, Changchun, China.","DOI":"10.1007\/978-3-642-14533-9_60"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jeddisaravi, K., Alitappeh, R.J., and Guimar\u00e3es, F.G. (2017, January 20). Multi-objective mobile robot path planning based on A* search. Proceedings of the International Conference on Computer & Knowledge Engineering, Mashhad, Iran.","DOI":"10.1109\/ICCKE.2016.7802107"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Karaman, S., and Frazzoli, E. (arXiv, 2011). Sampling-based algorithms for optimal motion planning, arXiv.","DOI":"10.15607\/RSS.2010.VI.034"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Richter, C., Bry, A., and Roy, N. (2016). Polynomial Trajectory Planning for Aggressive Quadrotor Flight in Dense Indoor Environments, Springer.","DOI":"10.1007\/978-3-319-28872-7_37"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.robot.2016.08.001","article-title":"Heuristic approaches in robot path planning: A survey","volume":"86","author":"Copot","year":"2016","journal-title":"Robot. Auton. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1109\/TRO.2015.2459812","article-title":"Path Planning for Single Unmanned Aerial Vehicle by Separately Evolving Waypoints","volume":"31","author":"Yang","year":"2015","journal-title":"IEEE Trans. Robot."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.swevo.2015.10.011","article-title":"A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning","volume":"28","author":"Das","year":"2016","journal-title":"Swarm Evol. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12555-016-0443-6","article-title":"Heterogeneous-ants-based path planner for global path planning of mobile robot applications","volume":"15","author":"Lee","year":"2017","journal-title":"Int. J. Control Autom. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1016\/j.neucom.2016.05.057","article-title":"A hybrid improved PSO-DV algorithm for multi-robot path planning in a clutter environment","volume":"207","author":"Das","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.knosys.2011.07.001","article-title":"A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example","volume":"26","author":"Pan","year":"2012","journal-title":"Knowl. Based Syst."},{"key":"ref_16","first-page":"1","article-title":"Progresses in Pigeon-inspired Optimization Algorithms","volume":"43","author":"Duan","year":"2017","journal-title":"J. Beijing Univ. Technol."},{"key":"ref_17","first-page":"18","article-title":"Algorithm for Low Altitude Penetration Aircraft Path Planning with Improved Ant Colony Algorithm","volume":"18","author":"Wen","year":"2005","journal-title":"Chin. J. Aeronaut."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cheng, J., Miao, Z., Li, B., and Xu, W. (2017, January 1\u20133). An improved ACO algorithm for mobile robot path planning. Proceedings of the IEEE International Conference on Information & Automation, Ningbo, China.","DOI":"10.1109\/ICInfA.2016.7831958"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, D., Xian, Y., Li, J., Lei, G., and Chang, Y. (2015, January 23\u201325). UAV Path Planning Based on Chaos Ant Colony Algorithm. Proceedings of the International Conference on Computer Science and Mechanical Automation, Hangzhou, China.","DOI":"10.1109\/CSMA.2015.23"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.asoc.2016.06.046","article-title":"A novel coordinated path planning method using k-degree smoothing for multi-UAVs","volume":"48","author":"Huang","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.asoc.2018.05.030","article-title":"A novel phase angle-encoded fruit fly optimization algorithm with mutation adaptation mechanism applied to UAV path planning","volume":"70","author":"Zhang","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1007\/s10922-016-9385-9","article-title":"A Survey of PSO-Based Scheduling Algorithms in Cloud Computing","volume":"25","author":"Masdari","year":"2017","journal-title":"J. Netw. Syst. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1109\/TII.2012.2198665","article-title":"Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning","volume":"9","author":"Roberge","year":"2012","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1109\/TSMC.2013.2248146","article-title":"Route Planning for Unmanned Aerial Vehicle (UAV) on the Sea Using Hybrid Differential Evolution and Quantum-Behaved Particle Swarm Optimization","volume":"43","author":"Fu","year":"2013","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Geng, Q., and Zhao, Z. (2013, January 25\u201327). A Kind of Route Planning Method for UAV Based on Improved PSO Algorithm. Proceedings of the Chinese Control and Decision Conference, Guiyang, China.","DOI":"10.1109\/CCDC.2013.6561326"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ayari, A., and Bouamama, S. (2017, January 27\u201331). Collision-free optimal paths for multiple robot systems using a new dynamic distributed particle swarm optimization algorithm. Proceedings of the International Conference on Advanced Robotics, Hefei\/Tai\u2019an, China.","DOI":"10.1109\/ICAR.2017.8023655"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1108\/IJICC-02-2014-0005","article-title":"Pigeon-inspired optimization: A new swarm intelligence optimizer for air robot path planning","volume":"7","author":"Duan","year":"2014","journal-title":"Int. J. Intell. Comput. Cybern."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, X., Duan, H., and Yang, C. (2014, January 8\u201310). Pigeon-Inspired optimization approach to multiple UAVs formation reconfiguration controller design. Proceedings of the Guidance, Navigation and Control Conference, Yantai, China.","DOI":"10.1109\/CGNCC.2014.7007594"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.cja.2014.12.008","article-title":"Gaussian pigeon-inspired optimization approach to orbital spacecraft formation reconfiguration","volume":"28","author":"Zhang","year":"2015","journal-title":"Chin. J. Aeronaut."},{"key":"ref_30","unstructured":"Ran, H., Luo, D., and Duan, H. (2014, January 8\u201310). Multiple UAVs mission assignment based on modified Pigeon-inspired optimization algorithm. Proceedings of the Guidance, Navigation and Control Conference, Yantai, China."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.ast.2016.11.012","article-title":"L\u00e9vy flight based pigeon-inspired optimization for control parameters optimization in automatic carrier landing system","volume":"61","author":"Dou","year":"2017","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, B., and Duan, H. (2014). Predator-Prey Pigeon-Inspired Optimization for UAV Three-Dimensional Path Planning. Advances in Swarm Intelligence, Springer.","DOI":"10.1007\/978-3-319-11897-0_12"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1109\/TCBB.2015.2443789","article-title":"Three-Dimensional Path Planning for Uninhabited Combat Aerial Vehicle Based on Predator-Prey Pigeon-Inspired Optimization in Dynamic Environment","volume":"14","author":"Zhang","year":"2017","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, H., and Duan, H. (2014, January 8\u201310). Bloch quantum-behaved Pigeon-inspired optimization for continuous optimization problems. Proceedings of the Guidance, Navigation & Control Conference, Yantai, China.","DOI":"10.1109\/CGNCC.2014.7007584"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liu, Z., Sun, H., and Hu, H. (2010, January 2\u20134). Two Sub-swarms Quantum-Behaved Particle Swarm Optimization Algorithm Based on Exchange Strategy. Proceedings of the Third International Symposium on Intelligent Information Technology & Security Informatics, Jinggangshan, China.","DOI":"10.1109\/IITSI.2010.64"},{"key":"ref_36","unstructured":"Sun, J., Feng, B., and Xu, W. (2004, January 19\u201323). Particle swarm optimization with particles having quantum behavior. Proceedings of the 2004 Congress on Evolutionary Computation, Portland, OR, USA."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/s00521-016-2324-6","article-title":"A path cost-based GRASP for minimum independent dominating set problem","volume":"28","author":"Wang","year":"2017","journal-title":"Neural Comput. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Himstedt, M., and Maehle, E. (2017). Online semantic mapping of logistic environments using RGB-D cameras. Int. J. Adv. Robot. Syst.","DOI":"10.1177\/1729881417720781"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1109\/4235.771166","article-title":"Parameter Control in Evolutionary Algorithms","volume":"3","author":"Eiben","year":"1999","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Bolaji, A.L., Babatunde, B.S., and Shola, P.B. (2018). Adaptation of Binary Pigeon-Inspired Algorithm for Solving Multidimensional Knapsack Problem. Soft Computing: Theories and Applications, Springer.","DOI":"10.1007\/978-981-10-5687-1_66"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"052204","DOI":"10.1007\/s11432-016-9115-2","article-title":"Path planning for mobile robot using self-adaptive learning particle swarm optimization","volume":"61","author":"Li","year":"2018","journal-title":"Sci. China Inf. Sci."},{"key":"ref_42","first-page":"487","article-title":"Coordinated UAV Path Planning Using Differential Evolution","volume":"5","author":"Nikolos","year":"2005","journal-title":"Oper. Res."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/12\/1\/3\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:35:36Z","timestamp":1760196936000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/12\/1\/3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,21]]},"references-count":42,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["a12010003"],"URL":"https:\/\/doi.org\/10.3390\/a12010003","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,12,21]]}}}