{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T06:00:30Z","timestamp":1781244030852,"version":"3.54.1"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:p>Mobile robots are widely used in various fields, including cosmic exploration, logistics delivery, and emergency rescue and so on. Path planning of mobile robots is essential for completing their tasks. Therefore, Path planning algorithms capable of finding their best path are needed. To address this challenge, we thus develop improved multi-objective artificial bee colony algorithm (IMOABC), a Bio-inspired algorithm-based approach for path planning. The IMOABC algorithm is based on multi-objective artificial bee colony algorithm (MOABC) with four strategies, including external archive pruning strategy, non-dominated ranking strategy, crowding distance strategy, and search strategy. IMOABC is tested on six standard test functions. Results show that IMOABC algorithm outperforms the other algorithms in solving complex multi-objective optimization problems. We then apply the IMOABC algorithm to path planning in the simulation experiment of mobile robots. IMOABC algorithm consistently outperforms existing algorithms (the MOABC algorithm and the ABC algorithm). IMOABC algorithm should be broadly useful for path planning of mobile robots.<\/jats:p>","DOI":"10.3389\/fnbot.2023.1196683","type":"journal-article","created":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T04:37:08Z","timestamp":1685594228000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Improved multi-objective artificial bee colony algorithm-based path planning for mobile robots"],"prefix":"10.3389","volume":"17","author":[{"given":"Qiuyu","family":"Cui","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengfei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hualong","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"He","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,6,1]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.swevo.2011.08.001","article-title":"A multi-objective artificial bee colony algorithm","volume":"2","author":"Akbari","year":"2012","journal-title":"Swarm Evol. Comput."},{"key":"B2","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1515\/cait-2015-0037","article-title":"An improved path planning method based on artificial potential field for a mobile robot","volume":"15","author":"Chen","year":"2015","journal-title":"Cybernet. Inform. Technol."},{"key":"B3","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1016\/j.asoc.2015.01.067","article-title":"Mobile robot path planning using artificial bee colony and evolutionary programming","volume":"30","author":"Contreras-Cruz","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"B4","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","article-title":"A fast and elitist multiobjective genetic algorithm: NSGA-II","volume":"6","author":"Deb","year":"2002","journal-title":"IEEE Trans. Evol. Comput."},{"key":"B5","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1109\/3477.484436","article-title":"Ant system: optimization by a colony of cooperating agents","volume":"26","author":"Dorigo","year":"1996","journal-title":"IEEE Trans. Syst. Man Cybernet Part B)"},{"key":"B6","doi-asserted-by":"publisher","first-page":"760","DOI":"10.1016\/j.advengsoft.2011.05.014","article-title":"jMetal: a Java framework for multi-objective optimization","volume":"42","author":"Durillo","year":"2011","journal-title":"Adv. Eng. Softw."},{"key":"B7","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.jart.2016.06.006","article-title":"Humanoid robot path planning with fuzzy Markov decision processes","volume":"14","author":"Fakoor","year":"2016","journal-title":"J. Appl. Res. Technol."},{"key":"B8","doi-asserted-by":"crossref","first-page":"2997","DOI":"10.1109\/IROS.2014.6942976","article-title":"Informed, R. R. T.:, \u201cOptimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic,\u201d","volume-title":"2014 IEEE\/RSJ International Conference on Intelligent Robots and Systems","author":"Gammell","year":"2014"},{"key":"B9","doi-asserted-by":"publisher","first-page":"4688","DOI":"10.1016\/j.eswa.2008.06.040","article-title":"Motion planning in order to optimize the length and clearance applying a Hopfield neural network","volume":"36","author":"Ghatee","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"B10","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1108\/IJIUS-07-2018-0021","article-title":"Optimal path planning of mobile robot using the hybrid cuckoo-bat algorithm in assorted environment","volume":"7","author":"Gunji","year":"2019","journal-title":"Int. J. Intell. Unmanned Syst."},{"key":"B11","first-page":"V5-277","article-title":"\u201cA MOABC for optimizing multi-objective problems,\u201d","volume-title":"2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE)","author":"Hedayatzadeh","year":"2010"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1729881419839575","DOI":"10.1177\/1729881419839575","article-title":"Mobile robot path planning in dynamic environment based on cuckoo optimization algorithm","volume":"16","author":"Hosseininejad","year":"2019","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"B13","doi-asserted-by":"publisher","first-page":"17500465","DOI":"10.1142\/S1793962317500465","article-title":"Simulation research for mobile robot path planning based on improved artificial potential field method recommended by the AsiaSim","volume":"8","author":"Hou","year":"2017","journal-title":"Int. J. Model. Simul. Sci. Comput."},{"key":"B14","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1109\/ICCKE.2016.7802107","article-title":"\u201cMulti-objective mobile robot path planning based on a search,\u201d","volume-title":"2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)","author":"Jeddisaravi","year":"2016"},{"key":"B15","volume-title":"An Idea Based on Honey Bee Swarm for Numerical Optimization","author":"Karaboga","year":"2005"},{"key":"B16","doi-asserted-by":"publisher","first-page":"846","DOI":"10.1177\/0278364911406761","article-title":"Sampling-based algorithms for optimal motion planning","volume":"30","author":"Karaman","year":"2011","journal-title":"Int. J. Rob. Res."},{"key":"B17","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1109\/ROBOT.2000.844730","article-title":"\u201cLaValle S M. RRT-connect: an efficient approach to single-query path planning,\u201d","volume-title":"Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065)","author":"Kuffner","year":"2000"},{"key":"B18","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.procs.2018.01.113","article-title":"Genetic algorithm based approach for autonomous mobile robot path planning","volume":"127","author":"Lamini","year":"2018","journal-title":"Proc. Comput. Sci."},{"key":"B19","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1177\/02783640122067453","article-title":"Randomized kinodynamic planning","volume":"20","author":"LaValle","year":"2001","journal-title":"Int. J. Robot. Res."},{"key":"B20","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1109\/ICRAE.2017.8291354","article-title":"\u201cAn improved artificial potential field method with a new point of attractive force for a mobile robot,\u201d","volume-title":"2017 2nd International Conference on Robotics and Automation Engineering (ICRAE)","author":"Lee","year":"2017"},{"key":"B21","unstructured":"\u201cPath planning of mobile robots based on improved Genetic algorithm,\u201d4953\n            LiuJ.\n            ChenZ.\n            ZhangY.\n            LiW.\n          ShanghaiAssociation for Computing MachineryProceedings of the 2020 2nd International Conference on Robotics, Intelligent Control and Artificial Intelligence2020"},{"key":"B22","doi-asserted-by":"publisher","first-page":"956","DOI":"10.1109\/TMECH.2022.3210592","article-title":"Control strategy of robot eye-head coordinate gaze behavior achieved for minimized neural transmission noise","volume":"28","author":"Liu","year":"2022","journal-title":"IEEE\/ASME Transact. Mechatr"},{"key":"B23","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.isatra.2020.12.015","article-title":"Two potential fields fused adaptive path planning system for autonomous vehicle under different velocities","volume":"112","author":"Liu","year":"2021","journal-title":"ISA Trans."},{"key":"B24","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.neucom.2012.07.060","article-title":"Research of biogeography particle swarm optimization for robot path planning","volume":"148","author":"Mo","year":"2015","journal-title":"Neurocomputing"},{"key":"B25","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1109\/INISTA.2011.5946136","article-title":"\u201cAn improved Tangent Bug method integrated with artificial potential field for multi-robot path planning,\u201d","volume-title":"2011 International Symposium on Innovations in Intelligent Systems and Applications","author":"Mohamed","year":"2011"},{"key":"B26","doi-asserted-by":"publisher","first-page":"299","DOI":"10.5772\/56718","article-title":"RRT*-SMART: a rapid convergence implementation of RRT","volume":"10","author":"Nasir","year":"2013","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"B27","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.eswa.2018.08.008","article-title":"Multi-objective multi-robot path planning in continuous environment using an enhanced genetic algorithm","volume":"115","author":"Nazarahari","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"B28","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1016\/j.neucom.2013.04.020","article-title":"An improved genetic algorithm with co-evolutionary strategy for global path planning of multiple mobile robots","volume":"120","author":"Qu","year":"2013","journal-title":"Neurocomputing"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.4340707","article-title":"Self-powered difunctional sensors based on sliding contact-electrification and tribovoltaic effects for pneumatic monitoring and controlling","author":"Shi","year":"2023","journal-title":"SSRN Electr. J"},{"key":"B30","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1109\/MFI.2012.6343040","article-title":"\u201cA fuzzy logic based bio-inspired system for mobile robot navigation,\u201d","volume-title":"2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","author":"Wang","year":"2012"},{"key":"B31","doi-asserted-by":"publisher","first-page":"615","DOI":"10.12783\/dtcse\/cmee2016\/5357","article-title":"Path planning of mobile robot based on genetic bee colony algorithm","volume":"16","author":"Wang","year":"2016","journal-title":"DEStech Transc Comput. Sci. Eng."},{"key":"B32","doi-asserted-by":"publisher","first-page":"450","DOI":"10.3390\/sym10100450","article-title":"Path planning for the mobile robot: a review","volume":"10","author":"Zhang","year":"2018","journal-title":"Symmetry"},{"key":"B33","doi-asserted-by":"publisher","first-page":"2733","DOI":"10.1007\/s00500-015-1977-x","article-title":"Enhancing the modified artificial bee colony algorithm with neighborhood search","volume":"21","author":"Zhou","year":"2017","journal-title":"Soft Comput."}],"container-title":["Frontiers in Neurorobotics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2023.1196683\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T04:37:20Z","timestamp":1685594240000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2023.1196683\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,1]]},"references-count":33,"alternative-id":["10.3389\/fnbot.2023.1196683"],"URL":"https:\/\/doi.org\/10.3389\/fnbot.2023.1196683","relation":{},"ISSN":["1662-5218"],"issn-type":[{"value":"1662-5218","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,1]]},"article-number":"1196683"}}