{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T18:39:09Z","timestamp":1764700749517,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2019,10,24]],"date-time":"2019-10-24T00:00:00Z","timestamp":1571875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Key R &amp; D project of Shandong Province","award":["No. 2018YFJH0704"],"award-info":[{"award-number":["No. 2018YFJH0704"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.: 51609120 and 51909127"],"award-info":[{"award-number":["No.: 51609120 and 51909127"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Science and Technology Plan for Shandong University","award":["No. J16LB7"],"award-info":[{"award-number":["No. J16LB7"]}]},{"name":"the Scienti\ufb01c Research Foundation of Chongqing Education Commission","award":["No. KJ1600509"],"award-info":[{"award-number":["No. KJ1600509"]}]},{"name":"the Foundation and Frontier Projects of Chongqing Science and Technology Commission","award":["No. cstc2016jcyjA0561"],"award-info":[{"award-number":["No. cstc2016jcyjA0561"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recently, issues of climate change, environment abnormality, individual requirements, and national defense have caused extensive attention to the commercial, scientific, and military development of unmanned surface vehicles (USVs). In order to design high-quality routes for a multi-sensor integrated USV, this work improves the conventional particle swarm optimization algorithm by introducing the greedy mechanism and the 2-opt operation, based on a combination strategy. First, a greedy black box is established for particle initialization, overcoming the randomness of the conventional method and excluding a great number of infeasible solutions. Then the greedy selection strategy and 2-opt operation are adopted together for local searches, to maintain population diversity and eliminate path crossovers. In addition, Monte-Carlo simulations of eight instances are conducted to compare the improved algorithm with other existing algorithms. The computation results indicate that the improved algorithm has the superior performance, with the shortest route and satisfactory robustness, although a fraction of computing efficiency becomes sacrificed. Moreover, the effectiveness and reliability of the improved method is also verified by its multi-sensor-based application to a USV model in real marine environments.<\/jats:p>","DOI":"10.3390\/s19214620","type":"journal-article","created":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T03:20:36Z","timestamp":1571973636000},"page":"4620","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Greedy Mechanism Based Particle Swarm Optimization for Path Planning Problem of an Unmanned Surface Vehicle"],"prefix":"10.3390","volume":"19","author":[{"given":"Junfeng","family":"Xin","sequence":"first","affiliation":[{"name":"College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China"}]},{"given":"Jiabao","family":"Zhong","sequence":"additional","affiliation":[{"name":"College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China"}]},{"given":"Shixin","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China"}]},{"given":"Jinlu","family":"Sheng","sequence":"additional","affiliation":[{"name":"Transport College, Chongqing Jiaotong University, Chongqing 400074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0822-7771","authenticated-orcid":false,"given":"Ying","family":"Cui","sequence":"additional","affiliation":[{"name":"College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.arcontrol.2016.04.018","article-title":"Unmanned Surface Vehicles: An Overview of Developments and Challenges","volume":"41","author":"Liu","year":"2016","journal-title":"Annu. Rev. Control"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"20","DOI":"10.4031\/MTSJ.44.4.5","article-title":"Bathy Boat: An Autonomous Surface Vessel for Stand-alone Survey and Underwater Vehicle Network Supervision","volume":"44","author":"Brown","year":"2010","journal-title":"Mar. Technol. Soc. J."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mu, D., Wang, G., Fan, Y., Sun, X., and Qiu, B. (2017). Adaptive Los Path Following for A Podded Propulsion Unmanned Surface Vehicle with Uncertainty of Model and Actual Saturation. Appl. Sci., 7.","DOI":"10.3390\/app7121232"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1550","DOI":"10.1016\/j.neucom.2017.09.088","article-title":"Efficient Multi-Task Allocation and Path Planning for Unmanned Surface Vehicle in Support of Ocean Operations","volume":"275","author":"Liu","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.oceaneng.2016.11.009","article-title":"A Multi-Layered Fast Marching Method for Unmanned Surface Vehicle Path Planning in A Time-Variant Maritime Environment","volume":"129","author":"Song","year":"2017","journal-title":"Ocean Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.neucom.2015.12.028","article-title":"A Real-Time Collision Avoidance Learning System for Unmanned Surface Vessels","volume":"182","author":"Zhao","year":"2002","journal-title":"Neurocomputing"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3321","DOI":"10.1177\/0142331218822698","article-title":"Feedback Motion Planning of Unmanned Surface Vehicles via Random Sequential Composition","volume":"41","author":"Ege","year":"2019","journal-title":"Trans. Inst. Meas. Control"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3960","DOI":"10.1007\/s11227-016-1739-2","article-title":"Finding approximate solutions of NP-hard optimization and TSP problems using elephant search algorithm","volume":"72","author":"Deb","year":"2016","journal-title":"J. Supercomput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2935","DOI":"10.1007\/s00521-017-2880-4","article-title":"A Hybrid Algorithm Using a Genetic Algorithm and Multiagent Reinforcement Learning Heuristic to Solve the Traveling Salesman Problem","volume":"30","author":"Alipour","year":"2017","journal-title":"Neural Comput. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.swevo.2018.02.017","article-title":"Discrete Comprehensive Learning Particle Swarm Optimization Algorithm with Metropolis Acceptance Criterion for Traveling Salesman Problem","volume":"42","author":"Zhong","year":"2018","journal-title":"Swarm Evol. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1184","DOI":"10.1166\/jctn.2015.3871","article-title":"An Improved Discrete Firefly Algorithm for the Traveling Salesman Problem","volume":"12","author":"Zhou","year":"2015","journal-title":"J. Comput. Theor. Nanosci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","article-title":"Grey Wolf Optimizer","volume":"69","author":"Mirjalili","year":"2014","journal-title":"Adv. Eng. Softw."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xin, J., Zhong, J., Yang, F., Cui, Y., and Sheng, J. (2019). An Improved Genetic Algorithm for Path-Planning of Unmanned Surface Vehicle. Sensors, 19.","DOI":"10.3390\/s19112640"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Xin, J., Li, S., Sheng, J., Zhang, Y., and Cui, Y. (2019). Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles. Sensors, 19.","DOI":"10.3390\/s19143096"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1007\/s00500-014-1522-3","article-title":"Hybrid Immune Algorithm based on Greedy Algorithm and Delete-Cross Operator for Solving TSP","volume":"20","author":"Pan","year":"2014","journal-title":"Soft Comput."},{"key":"ref_16","first-page":"134","article-title":"Efficient Preprocessing Methods for Tabu Search: An Application on Asymmetric Travelling Salesman Problem","volume":"55","author":"Basu","year":"2017","journal-title":"INFOR Inf. Syst. Oper. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1590\/0101-7438.2016.036.01.0113","article-title":"A Hybrid Heuristic Algorithm for the Clustered Traveling Salesman Problem","volume":"36","author":"Mestria","year":"2016","journal-title":"Pesquisa Operacional"},{"key":"ref_18","unstructured":"Kennedy, J., and Eberhart, R.C. (December, January 27). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1016\/j.asoc.2007.05.005","article-title":"Molecular Docking with Multi-Objective Particle Swarm Optimization","volume":"8","author":"Janson","year":"2007","journal-title":"Appl. Soft Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1243\/09544054JEM1158","article-title":"Multi-Objective Optimization of Electrochemical Machining Process Parameters Using A Particle Swarm Optimization Algorithm","volume":"222","author":"Rao","year":"2008","journal-title":"J. Eng. Manuf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/TASE.2008.917053","article-title":"Contrast Enhancement and Intensity Preservation for Gray-Level Images Using Multiobjective Particle Swarm Optimization","volume":"6","author":"Kwok","year":"2009","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1016\/j.ejor.2005.11.020","article-title":"Solving the Semi-Desirable Facility Location Problem Using Bi-Objective Particle Swarm","volume":"177","author":"Yapicioglu","year":"2006","journal-title":"Eur. J. Oper. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.1016\/j.matdes.2009.10.050","article-title":"Multi-Objective Robust Optimization Method for Drawbead Design in Sheet Metal Forming","volume":"31","author":"Sun","year":"2010","journal-title":"Mater. Des."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1007\/s10489-009-0179-6","article-title":"Study on Hybrid PS-ACO Algorithm","volume":"34","author":"Shuang","year":"2009","journal-title":"Appl. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1016\/j.asoc.2015.01.068","article-title":"A New Hybrid Method based on Particle Swarm Optimization, Ant Colony Optimization and 3-Opt Algorithms for Traveling Salesman Problem","volume":"30","author":"Mahi","year":"2015","journal-title":"Soft Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1504\/IJBIC.2010.030042","article-title":"Hybrid Particle Swarm Optimization with K-Centres Method and Dynamic Velocity Range Setting for Travelling Salesman Problems","volume":"2","author":"Zhang","year":"2010","journal-title":"Int. J. Bio Inspir. Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.ins.2014.02.098","article-title":"Hybrid Evolutionary Fuzzy Learning Scheme in the Applications of Traveling Salesman Problems","volume":"270","author":"Feng","year":"2014","journal-title":"Inf. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1707","DOI":"10.1007\/s00500-012-0855-z","article-title":"A Novel Two-Stage Hybrid Swarm Intelligence Optimization Algorithm and Application","volume":"16","author":"Deng","year":"2012","journal-title":"Soft Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6327482","DOI":"10.1155\/2017\/6327482","article-title":"The Application of PSO-AFSA Method in Parameter Optimization for Underactuated Autonomous Underwater Vehicle Control","volume":"2017","author":"Jiang","year":"2017","journal-title":"Math. Probl. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1847","DOI":"10.1111\/itor.12406","article-title":"A Hybrid Heuristic Approach to Minimize Number of Tardy Jobs in Group Technology Systems","volume":"26","author":"Bajwa","year":"2017","journal-title":"Int. Trans. Oper. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.ins.2018.12.086","article-title":"A Multi-Adaptive Particle Swarm Optimization for the Vehicle Routing Problem with Time Windows","volume":"481","author":"Marinakis","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/s12647-017-0225-5","article-title":"Effective Form Error Assessment Using Improved Particle Swarm Optimization","volume":"32","author":"Pathak","year":"2017","journal-title":"MAPAN"},{"key":"ref_33","first-page":"2567","article-title":"A Capacitated Bike Sharing Location-Allocation Problem under Demand Uncertainty Using Sample Average Approximation: A Greedy Genetic-Particle Swarm Optimization Algorithm","volume":"24","author":"Askari","year":"2017","journal-title":"Sci. Iran."},{"key":"ref_34","unstructured":"Shi, Y., and Eberhart, R.C. (1998, January 4\u20136). A Modified Particle Swarm Optimizer. Proceedings of the IEEE International Conference of Evolutionary Computation, Anchorage, AK, USA."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1016\/j.cor.2009.03.004","article-title":"A Hybrid Multi-Swarm Particle Swarm Optimization algorithm for the Probabilistic Traveling Salesman Problem","volume":"37","author":"Marinakis","year":"2010","journal-title":"Comput. Oper. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1287\/opre.6.6.791","article-title":"A Method for Solving Traveling-Salesman Problems","volume":"6","author":"Croes","year":"1958","journal-title":"Oper. Res."},{"key":"ref_37","unstructured":"Spear, M.E. (1952). Charting Statistics, McGraw-Hill."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/21\/4620\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:28:53Z","timestamp":1760189333000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/21\/4620"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,24]]},"references-count":37,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["s19214620"],"URL":"https:\/\/doi.org\/10.3390\/s19214620","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,10,24]]}}}