{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T15:38:35Z","timestamp":1779291515630,"version":"3.51.4"},"reference-count":57,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,2,28]],"date-time":"2019-02-28T00:00:00Z","timestamp":1551312000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Evacuation is an important activity for reducing the number of casualties and amount of damage in disaster management. Evacuation planning is tackled as a spatial optimization problem. The decision-making process for evacuation involves high uncertainty, conflicting objectives, and spatial constraints. This study presents a Multi-Objective Artificial Bee Colony (MOABC) algorithm, modified to provide a better solution to the evacuation problem. The new approach combines random swap and random insertion methods for neighborhood search, the two-point crossover operator, and the Pareto-based method. For evacuation planning, two objective functions were considered to minimize the total traveling distance from an affected area to shelters and to minimize the overload capacity of shelters. The developed model was tested on real data from the city of Kigali, Rwanda. From computational results, the proposed model obtained a minimum fitness value of 5.80 for capacity function and 8.72 \u00d7 108 for distance function, within 161 s of execution time. Additionally, in this research we compare the proposed algorithm with Non-Dominated Sorting Genetic Algorithm II and the existing Multi-Objective Artificial Bee Colony algorithm. The experimental results show that the proposed MOABC outperforms the current methods both in terms of computational time and better solutions with minimum fitness values. Therefore, developing MOABC is recommended for applications such as evacuation planning, where a fast-running and efficient model is needed.<\/jats:p>","DOI":"10.3390\/ijgi8030110","type":"journal-article","created":{"date-parts":[[2019,3,1]],"date-time":"2019-03-01T03:33:00Z","timestamp":1551411180000},"page":"110","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Evacuation Planning Optimization Based on a Multi-Objective Artificial Bee Colony Algorithm"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5690-6106","authenticated-orcid":false,"given":"Olive","family":"Niyomubyeyi","sequence":"first","affiliation":[{"name":"Department of Physical Geography and Ecosystem Science, Lund University, SE-221 00 Lund, Sweden"},{"name":"Center for Geographic Information System and Remote Sensing, College of Science and Technology, University of Rwanda, Kigali 4285, Rwanda"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Petter","family":"Pilesj\u00f6","sequence":"additional","affiliation":[{"name":"Department of Physical Geography and Ecosystem Science, Lund University, SE-221 00 Lund, Sweden"},{"name":"Center for Middle Eastern Studies, Lund University, SE-221 00 Lund, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6812-4307","authenticated-orcid":false,"given":"Ali","family":"Mansourian","sequence":"additional","affiliation":[{"name":"Department of Physical Geography and Ecosystem Science, Lund University, SE-221 00 Lund, Sweden"},{"name":"Center for Middle Eastern Studies, Lund University, SE-221 00 Lund, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dilley, M., Chen, R.S., Deichmann, U., Lerner-Lam, A.L., and Arnold, M. (2005). Natural Disaster Hotspots: A Global Risk Analysis, The World Bank.","DOI":"10.1596\/0-8213-5930-4"},{"key":"ref_2","unstructured":"Guha-Sapir, D., Hoyois, P., Wallemacq, P., and Below, R. (2016). Annual Disaster Statistical Review 2016: The Numbers and Trends. Brussels: CRED, Centre for Research on the Epidemiology of Disasters (CRED), Institute of Health and Society (IRSS)."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yusoff, M., Ariffin, J., and Mohamed, A. (2008, January 26\u201328). Optimization approaches for macroscopic emergency evacuation planning: A survey. Proceedings of the 2008 International Symposium on Information Technology, Kuala Lumpur, Malaysia.","DOI":"10.1109\/ITSIM.2008.4631982"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.ejor.2008.07.032","article-title":"Evacuation planning using multiobjective evolutionary optimization approach","volume":"198","author":"Saadatseresht","year":"2009","journal-title":"Eur. J. Oper. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1111\/j.1467-7717.2010.01171.x","article-title":"Optimizing hurricane disaster relief goods distribution: Model development and application with respect to planning strategies","volume":"34","author":"Horner","year":"2010","journal-title":"Disasters"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.seps.2011.04.004","article-title":"Optimization models in emergency logistics: A literature review","volume":"46","author":"Caunhye","year":"2012","journal-title":"Socio-Econ. Plan. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kuligowski, E.D., and Peacock, R.D. (2005). A Review of Building Evacuation Models.","DOI":"10.6028\/NIST.TN.1471"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1016\/j.ejor.2008.08.025","article-title":"Multi-objective evacuation routing in transportation networks","volume":"198","author":"Stepanov","year":"2009","journal-title":"Eur. J. Oper. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.trc.2012.11.005","article-title":"Evacuation transportation modeling: An overview of research, development, and practice","volume":"27","author":"Wolshon","year":"2013","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1080\/13658810600661508","article-title":"GIS-based multicriteria decision analysis: A survey of the literature","volume":"20","author":"Malczewski","year":"2006","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Malczewski, J., and Rinner, C. (2015). Multicriteria Decision Analysis in Geographic Information Science, Springer.","DOI":"10.1007\/978-3-540-74757-4"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Talbi, E.-G. (2009). Metaheuristics: From Design to Implementation, John Wiley & Sons.","DOI":"10.1002\/9780470496916"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1290","DOI":"10.1080\/00045608.2012.685044","article-title":"Spatial Optimization in Geography","volume":"102","author":"Tong","year":"2012","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.ejor.2014.11.030","article-title":"Models, solutions and enabling technologies in humanitarian logistics","volume":"244","author":"Ertem","year":"2015","journal-title":"Eur. J. Oper. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1111\/j.1538-4632.2009.00745.x","article-title":"A Multiobjective Approach to Locate Emergency Shelters and Identify Evacuation Routes in Urban Areas","volume":"41","author":"Santos","year":"2009","journal-title":"Geogr. Anal."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.jtrangeo.2012.01.006","article-title":"Solving a location-routing problem with a multiobjective approach: The design of urban evacuation plans","volume":"22","year":"2012","journal-title":"J. Transp. Geogr."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1111\/disa.12233","article-title":"Special needs hurricane shelters and the ageing population: Development of a methodology and a case study application","volume":"42","author":"Horner","year":"2018","journal-title":"Disasters"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1016\/j.ijdrr.2017.12.006","article-title":"Pet- and special needs-friendly shelter planning in south Florida: A spatial capacitated p-median-based approach","volume":"31","author":"Kocatepe","year":"2018","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_19","unstructured":"Brownlee, J. (2012). Clever Algorithms: Nature-Inspired Programming Recipes, LuLu.com s.l.. Revision 2."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.swevo.2011.03.001","article-title":"Multiobjective evolutionary algorithms: A survey of the state of the art","volume":"1","author":"Zhou","year":"2011","journal-title":"Swarm Evol. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"15","DOI":"10.3390\/a7010015","article-title":"Bio-Inspired Meta-Heuristics for Emergency Transportation Problems","volume":"7","author":"Zhang","year":"2014","journal-title":"Algorithms"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1007\/BF02826478","article-title":"Optimization of land use structure based on ecological GREEN equivalent","volume":"5","author":"Yanfang","year":"2002","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1007\/BF02826603","article-title":"The decision of the optimal parameters in Markov random fields of images by genetic algorithm","volume":"3","author":"Zhaobao","year":"2000","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s11806-007-0053-9","article-title":"Model of land suitability evaluation based on computational intelligence","volume":"10","author":"Jiao","year":"2007","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1109\/JSTARS.2013.2280697","article-title":"Spatial Multi-Objective Optimization Approach for Land Use Allocation Using NSGA-II","volume":"7","author":"Shaygan","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1080\/10095020.2018.1489576","article-title":"An improved knowledge-informed NSGA-II for multi-objective land allocation (MOLA)","volume":"21","author":"Song","year":"2018","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1007\/s11806-011-0437-8","article-title":"Land-use spatial optimization based on PSO algorithm","volume":"14","author":"Ma","year":"2011","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1080\/10095020.2015.1017910","article-title":"Land-use zoning in fast developing coastal area with ACO model for scenario decision-making","volume":"18","author":"Ai","year":"2015","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1080\/10095020.2012.708151","article-title":"A novel GIS-based decision-making framework for the school bus routing problem","volume":"15","author":"Eldrandaly","year":"2012","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1080\/13658816.2015.1041959","article-title":"A unified approach for location-allocation analysis: Integrating GIS, distributed computing and spatial optimization","volume":"30","author":"Lei","year":"2016","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1470","DOI":"10.1080\/13658816.2015.1012512","article-title":"An improved artificial bee colony algorithm for optimal land-use allocation","volume":"29","author":"Yang","year":"2015","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.tre.2014.08.007","article-title":"A comprehensive evacuation planning model and genetic solution algorithm","volume":"71","author":"Goerigk","year":"2014","journal-title":"Transp. Res. Part E Logist. Transp. Rev."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1016\/j.asoc.2014.09.041","article-title":"Evolutionary optimization for disaster relief operations: A survey","volume":"27","author":"Zheng","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_34","unstructured":"Garrett, A., Carnahan, B., Muhdi, R., Davis, J., Dozier, G., SanSoucie, M.P., Hull, P.V., and Tinker, M.L. (2006, January 16\u201321). Evacuation Planning via Evolutionary Computation. Proceedings of the 2006 IEEE International Conference on Evolutionary Computation, Vancouver, BC, Canada."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"792","DOI":"10.1016\/j.jhazmat.2010.02.010","article-title":"Multi-objective evolutionary emergency response optimization for major accidents","volume":"178","author":"Georgiadou","year":"2010","journal-title":"J. Hazard. Mater."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1643","DOI":"10.1080\/13658816.2011.643802","article-title":"A modified particle swarm optimization algorithm for optimal allocation of earthquake emergency shelters","volume":"26","author":"Hu","year":"2012","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1080\/13658816.2017.1395882","article-title":"A comparison of scenario-based hybrid bilevel and multi-objective location-allocation models for earthquake emergency shelters: A case study in the central area of Beijing, China","volume":"32","author":"Xu","year":"2018","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1007\/978-3-642-13495-1_78","article-title":"Multi-Objective Optimization for Massive Pedestrian Evacuation Using Ant Colony Algorithm","volume":"Volume 6145","author":"Tan","year":"2010","journal-title":"Advances in Swarm Intelligence"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Saeidian, B., Mesgari, M.S., Pradhan, B., and Ghodousi, M. (2018). Optimized Location-Allocation of Earthquake Relief Centers Using PSO and ACO, Complemented by GIS, Clustering, and TOPSIS. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7080292"},{"key":"ref_40","unstructured":"Karaboga, D. (2005). An Idea Based on Honey Bee Swarm for Numerical Optimization, Erciyes University, Engineering Faculty, Computer Engineering Department."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s10462-012-9328-0","article-title":"A comprehensive survey: Artificial bee colony (ABC) algorithm and applications","volume":"42","author":"Karaboga","year":"2014","journal-title":"Artif. Intell. Rev."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2020","DOI":"10.1080\/13658816.2017.1346795","article-title":"An artificial bee colony-based multi-objective route planning algorithm for use in pedestrian navigation at night","volume":"31","author":"Fang","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.asoc.2016.11.014","article-title":"An Artificial Bee Colony Algorithm for Multi-objective Optimisation","volume":"50","author":"Luo","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yang, L., Zhu, A., Shao, J., and Chi, T. (2018). A Knowledge-Informed and Pareto-Based Artificial Bee Colony Optimization Algorithm for Multi-Objective Land-Use Allocation. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7020063"},{"key":"ref_45","doi-asserted-by":"crossref","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":"ref_46","unstructured":"Coello, C.A.C., Lamont, G.B., and Veldhuizen, D.A.V. (2007). Evolutionary Algorithms for Solving Multi-Objective Problems, Springer. [2nd ed.]."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2578","DOI":"10.1016\/j.apm.2011.09.041","article-title":"A hybrid multi-objective artificial bee colony algorithm for burdening optimization of copper strip production","volume":"36","author":"Zhang","year":"2012","journal-title":"Appl. Math. Model."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.cie.2014.06.012","article-title":"A hybrid artificial bee colony for disruption in a hierarchical maximal covering location problem","volume":"75","author":"Farahani","year":"2014","journal-title":"Comput. Ind. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.asoc.2015.03.040","article-title":"Elite-guided multi-objective artificial bee colony algorithm","volume":"32","author":"Huo","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/s10109-010-0136-2","article-title":"Evolutionary Multi-objective Optimization for landscape system design","volume":"13","author":"Roberts","year":"2011","journal-title":"J. Geogr. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","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":"ref_52","unstructured":"(2018, November 22). NISR Population and Housing Census of Rwanda, 2012\u2014Rwanda Data Portal. Available online: http:\/\/rwanda.opendataforafrica.org\/\/pkzmyhf\/population-and-housing-census-of-rwanda-2012."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Bizimana, J.P., and Schilling, M. (2009). Geo-Information Technology for Infrastructural Flood Risk Analysis in Unplanned Settlements: A case study of informal settlement flood risk in the Nyabugogo flood plain, Kigali City, Rwanda. Geospatial Techniques in Urban Hazard and Disaster Analysis, Springer.","DOI":"10.1007\/978-90-481-2238-7_6"},{"key":"ref_54","unstructured":"MIDIMAR (2015). The National Risk Atlas of Rwanda."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1007\/s12063-010-0028-0","article-title":"Decision support for disaster management","volume":"3","author":"Rolland","year":"2010","journal-title":"Oper. Manag. Res."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"The Sphere Project (2011). Humanitarian Charter and Minimum Standards in Humanitarian Response: The Sphere Handbook, The Sphere Project.","DOI":"10.3362\/9781908176202"},{"key":"ref_57","first-page":"2171","article-title":"DEAP: Evolutionary Algorithms Made Easy","volume":"13","author":"Fortin","year":"2012","journal-title":"J. Mach. Learn. Res."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/3\/110\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:35:17Z","timestamp":1760186117000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/3\/110"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,28]]},"references-count":57,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["ijgi8030110"],"URL":"https:\/\/doi.org\/10.3390\/ijgi8030110","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,28]]}}}