Optimal spatial land-use allocation for limited development ecological zones based on the geographic information system and a genetic ant colony algorithm

References

• Aerts, J.C.J.H., Goodchild, M.F., and Heuvelink, G.B.M., 2003. Accounting for spatial uncertainty in optimization with spatial decision support systems. Transactions in GIS, 7 (2), 211–230. doi:10.1111/1467-9671.00141
   Google Scholar
• Balling, R.J., Brown, M.R., and Day, K., 1999. Multiobjective urban planning using genetic algorithm. Journal of Urban Planning and Development, 125 (2), 86–99. doi:10.1061/(ASCE)0733-9488(1999)125:2(86)
   Web of Science ®Google Scholar
• Cao, K. and Ye, X.Y., 2013. Coarse-grained parallel genetic algorithm applied to a vector-based land use allocation optimization problem: the case study of Tongzhou Newtown, Beijing, China. Stochastic Environmental Research and Risk Assessment: Research Journal, 27, 1133–1142. doi:10.1007/s00477-012-0649-y
   Web of Science ®Google Scholar
• Cao, K., et al., 2012. Sustainable land use optimization using boundary-based fast genetic algorithm. Computers, Environment and Urban Systems, 36 (3), 257–269. doi:10.1016/j.compenvurbsys.2011.08.001
   Web of Science ®Google Scholar
• Carver, S., 1991. Integrating multi-criteria evaluation with geographical information systems. International Journal of Geographical Information Systems, 5 (3), 321–339. doi:10.1080/02693799108927858
   Web of Science ®Google Scholar
• Chandramouli, M., Huang, B., and Xue, L., 2009. Spatial change optimization: integrating GA with visualization for 3D scenario generation. Photogrammetric Engineering & Remote Sensing, 75 (8), 1015–1022. doi:10.14358/PERS.75.8.1015
   Web of Science ®Google Scholar
• Chuvieco, E., 1993. Integration of linear programming and GIS for land-use modelling. International Journal of Geographical Information Systems, 7, 71–83. doi:10.1080/02693799308901940
   Web of Science ®Google Scholar
• Croteau, K.G., Faber, B.G., and Vernon, L.T., 1997. Smart places: a tool for design and evaluation of land use scenarios. In: Proceedings of the 1997 ESRI User Conference (CDROM). San Diego, CA: Environmental Systems Research Institute.
   Google Scholar
• Dai, F.C., Lee, C.F., and Zhang, X.H., 2001. GIS-based geo-environmental evaluation for urban land-use planning: a case study. Engineering Geology, 61 (4), 257–271. doi:10.1016/S0013-7952(01)00028-X
   Web of Science ®Google Scholar
• Datta, D., 2007. Multi-objective evolutionary algorithms for resource allocation problems. Thesis (PhD). Department of Mechanical Engineering, Indian Institute of Technology Kanpur (IIT-Kanpur), India.
   Google Scholar
• Datta, D., et al., 2007. Multi-objective evolutionary algorithm for land-use management problem. International Journal of Computational Intelligence Research, 3 (4), 371–384.
   Google Scholar
• Datta, D., Fonseca, C.M., and Deb, K., 2008. A multi-objective evolutionary algorithm to exploit the similarities of resource allocation problems. Journal of Scheduling, 11, 405–419. doi:10.1007/s10951-008-0073-9
   Web of Science ®Google Scholar
• Dorigo, M., Maniezzo, V., and Colorni, A., 1996. Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 26 (1), 29–41. doi:10.1109/3477.484436
   PubMed Web of Science ®Google Scholar
• Eastman, J.R., et al., 1995. Raster procedures for multi-criteria/multi-objective decisions. Photogrammetric Engineering and Remote Sensing, 61 (5), 539–547.
   Web of Science ®Google Scholar
• Fischer, G., Granat, J., and Makowski, M., 1998. An interactive multiple-criteria analysis tool for land resources appraisal. Laxenburg: International Institute for Applied Systems Analysis, Interim Report IR-98-051.
   Google Scholar
• Gong, J.Z., Liu, Y.S., and Chen, W.L., 2012. Optimal land use allocation of urban fringe in Guangzhou. Journal Geographic Sciences, 22 (1), 179–191. doi:10.1007/s11442-012-0920-7
   Web of Science ®Google Scholar
• Han, J.C., et al., 2013. Optimal land use management for soil erosion control by using an interval-parameter fuzzy two-stage stochastic programming approach. Environmental Management, 52, 621–638. doi:10.1007/s00267-013-0122-9
   PubMed Web of Science ®Google Scholar
• He, J.Q., et al., 2009. Ant colony algorithms for optimal site selection in large regions. Journal Remote Sensing, 13 (2), 246–256.
   Google Scholar
• Hou, J.-W., Mi, W.-B., and Li, L.-T., 2014a. Spatial quality evaluation for drinking water based on GIS and ant colony clustering algorithm. Journal of Central South University of Technology, 21 (3), 1051–1057. doi:10.1007/s11771-014-2036-y
   Web of Science ®Google Scholar
• Hou, J.W., Mi, W.B., and Sun, J.L., 2014b. Optimal spatial allocation of water resources based on Pareto ant colony algorithm. International Journal of Geographical Information Science, 28 (2), 213–233. doi:10.1080/13658816.2013.849809
   Web of Science ®Google Scholar
• Hu, H.L., et al., 2011. Integration of a site selection model with the multi-agent system and the ant colony algorithm and its application to Changsha. Resources Sciences, 33 (6), 1211–1217.
   Google Scholar
• Huang, B., Cheu, R.L., and Liew, Y.S., 2004. GIS and genetic algorithms for HazMat route planning with security considerations. International Journal of Geographical Information Science, 18 (8), 769–787. doi:10.1080/13658810410001705307
   Web of Science ®Google Scholar
• Huang, B., Liu, N., and Chandramouli, M., 2006b. A GIS-supported ant algorithm for the linear feature covering problem with distance constraints. Decision Support Systems, 42 (2), 1063–1075. doi:10.1016/j.dss.2005.09.002
   Web of Science ®Google Scholar
• Huang, B., Yao, L., Raguraman, K., 2006a. A bi-level GA and GIS for multi-objective TSP route planning. Transportation Planning and Technology, 29 (2), 105–124. 10.1080/03081060600753404
   Web of Science ®Google Scholar
• Huang, B., Zhang, L., Wu, B., 2009b. Spatio-temporal analysis of rural-urban land conversion. International Journal of Geographical Information Science, 23 (3), 379–398. 10.1080/13658810802119685
   Web of Science ®Google Scholar
• Huang, B. and Zhang, W.T., 2014. Sustainable land-use planning for a Downtown Lake Area in Central China: multiobjective optimization approach aided by urban growth modeling. ASCE Journal of Urban Planning and Development, 140 (2), 04014002. doi:10.1061/(ASCE)UP.1943-5444.0000186
   Web of Science ®Google Scholar
• Huang, B., et al., 2008. Seeking the Pareto front for multiobjective spatial optimization problems. International Journal of Geographical Information Science, 22 (5), 507–526. doi:10.1080/13658810701492365
   Web of Science ®Google Scholar
• Huang, B., et al., 2009a. Land use change modeling using unbalanced support vector machines. Environment and Planning B: Planning and Design, 36 (3), 398–416. doi:10.1068/b33047
   Web of Science ®Google Scholar
• Huang, K.N., et al., 2013. An improved artificial immune system for seeking the Pareto front of land use allocation problem in large areas. International Journal of Geographical Information Science, 27 (5), 922–946. doi:10.1080/13658816.2012.730147
   Web of Science ®Google Scholar
• Huang, Q.-H. and Cai, Y.-L., 2007. Simulation of land use change using GIS-based stochastic model: the case study of Shiqian County, Southwestern China. Stochastic Environmental Research and Risk Assessment: Research Journal, 21 (4), 419–426. doi:10.1007/s00477-006-0074-1
   Web of Science ®Google Scholar
• Kamusoko, C., et al., 2009. Rural sustainability under threat in Zimbabwe–simulation of future land use/cover changes in the Bindura district based on the Markovcellular automata model. Applied Geography, 29 (3), 435–447. doi:10.1016/j.apgeog.2008.10.002
   Web of Science ®Google Scholar
• Klein, T., et al., 2013. Adapting agricultural land management to climate change: a regional multi-objective optimization approach. Landscape Ecology, 28, 2029–2047. doi:10.1007/s10980-013-9939-0
   Web of Science ®Google Scholar
• Kwartler, M. and Bernard, R.N., 2001. CommunityViz: an integrated planning support system. Redlands, CA: ESRI Press, 285–308.
   Google Scholar
• Ligmann, Z.A., Church, R.L., and Jankowski, P., 2008. Spatial optimization as a generative technique for sustainable multiobjective land-use allocation. International Journal of Geographical Information Science, 22 (6), 601–622. doi:10.1080/13658810701587495
   Web of Science ®Google Scholar
• Liu, N., Huang, B., and Chandramouli, M., 2006. Optimal siting of fire stations using GIS and ANT algorithm. ASCE Journal of Computing in Civil Engineering, 20 (5), 361–369. doi:10.1061/(ASCE)0887-3801(2006)20:5(361)
   Web of Science ®Google Scholar
• Liu, S., Wang, H.Y., and Lang, L., 2011. A novel approach of hybrid genetic optimization with elite ant colony: application in the modulation recognition. Wuhan University Journal of Natural Sciences, 16 (1), 43–48. doi:10.1007/s11859-011-0709-z
   Google Scholar
• Liu, X.P., Lao, C.H., and Li, X., 2012a. An integrated approach of remote sensing, GIS and swarm intelligence for zoning protected ecological areas. Landscape Ecology, 27, 447–463. doi:10.1007/s10980-011-9684-1
   Web of Science ®Google Scholar
• Liu, X.P., Li, X., and Yeh, A.G.O., 2007c. Discovery of transition rules for geographical cellular automata by using ant colony optimization. Science in China Series D: Earth Sciences, 50 (10), 1578–1588. doi:10.1007/s11430-007-0083-z
   Google Scholar
• Liu, X.P., et al., 2007a. Geographical CA based on ant colony optimization. In: Conference proceeding of the second geo-CA workshop as well as workshop of GIS theory and method committee of Chinese GIS association, 1–3 December. Guangzhou: Committee of Chinese GIS Association, 317–325.
   Google Scholar
• Liu, X.P., et al., 2007b. Digging the rules of geo-CA based on ant colony optimization. Sciences China D Editors, 37 (6), 824–834.
   Google Scholar
• Liu, X.P., et al., 2008. A bottom-up approach to discover transition rules of cellular automata using ant intelligence. International Journal of Geographical Information Science, 22 (11–12), 1247–1269. doi:10.1080/13658810701757510
   Web of Science ®Google Scholar
• Liu, X.P., et al., 2012b. A multi-type ant colony optimization (MACO) method for optimal land use allocation in large areas. International Journal of Geographical Information Science, 26 (7), 1325–1343. doi:10.1080/13658816.2011.635594
   Web of Science ®Google Scholar
• Liu, Y.F., Li, X.L., and Gong, H.B., 2005. Optimization for land use structure based on genetic algorithms. Geomatics and Information Science of Wuhan University, 30 (4), 288–292. in Chinese.
   Google Scholar
• Liu, Y., et al., 2007d. ICCLP: an inexact chance-constrained linear programming model for land-use management of lake areas in urban fringes. Environmental Management, 40, 966–980. doi:10.1007/s00267-007-9013-2
   PubMed Web of Science ®Google Scholar
• Matthews, K.B., Sibbald, A.R., and Craw, S., 1999. Implementation of a spatial decision support system for rural land use planning: integrating geographic information system and environmental models with search and optimisation algorithms. Computers and Electronics in Agriculture, 23 (1), 9–26. doi:10.1016/S0168-1699(99)00005-8
   Web of Science ®Google Scholar
• McDonald, J.F., 2001. Cost-benefit analysis of local land use allocation decisions. Journal of Regional Science, 41 (2), 277–299. doi:10.1111/0022-4146.00217
   Web of Science ®Google Scholar
• Mendoza, G.A., 1987. A mathematical model for generating land-use allocation alternatives for agroforestry systems. Agroforestry Systems, 5 (4), 443–453. doi:10.1007/BF00047178
   Web of Science ®Google Scholar
• Meyer, B.C., Lescot, J.-M., and Laplana, R., 2009. Comparison of two spatial optimization techniques: a framework to solve multiobjective land use distribution problems. Environmental Management, 43, 264–281. doi:10.1007/s00267-008-9225-0
   PubMed Web of Science ®Google Scholar
• Mitsova, D., Shuster, W., and Wang, X.H., 2011. A cellular automata model of land cover change to integrate urban growth with open space conservation. Landscape and Urban Planning, 99 (2), 141–153. doi:10.1016/j.landurbplan.2010.10.001
   Web of Science ®Google Scholar
• Pradhan, S. and Lam, S.S.Y., 2007. Minimizing makespan during environmental stress screening using a genetic algorithm and an ant colony optimization. International Journal Advancement Manuf Technological, 32, 571–577. doi:10.1007/s00170-005-0346-9
   Web of Science ®Google Scholar
• Roetter, R.P., et al., 2005. Integration of systems network (SysNet) tools for regional land use scenario analysis in Asia. Environmental Modelling & Software, 20 (3), 291–307. doi:10.1016/j.envsoft.2004.01.001
   Web of Science ®Google Scholar
• Rossing, W.A.H., et al., 2007. Integrative modelling approaches for analysis of impact of multifunctional agriculture: a review for France, Germany and The Netherlands. Agriculture, Ecosystems and Environment, 120 (1), 41–57. doi:10.1016/j.agee.2006.05.031
   Web of Science ®Google Scholar
• Sante, R.I., Crecente, M.R., and Miranda, B.D., 2008. GIS-based planning support system for rural land-use allocation. Computers and Electronics in Agriculture, 63 (2), 257–273. doi:10.1016/j.compag.2008.03.007
   Web of Science ®Google Scholar
• Sharawi, H.A., 2006. Optimal land-use allocation in central Sudan. Forest Policy and Economics, 8 (1), 10–21. doi:10.1016/j.forpol.2004.04.006
   Web of Science ®Google Scholar
• Stewart, T.J., Janssen, R., and Van, H.M., 2004. A genetic algorithm approach to multiobjective land use planning. Computers & Operations Research, 31 (14), 2293–2313. doi:10.1016/S0305-0548(03)00188-6
   Web of Science ®Google Scholar
• Waddell, P., 2002. UrbanSim: modeling urban development for land use, transportation, and environmental planning. Journal of the American Planning Association, 68 (3), 297–314. doi:10.1080/01944360208976274
   Web of Science ®Google Scholar
• Wang, H.R., et al., 2010. Land use allocation based on interval multi-objective linear programming model: a case study of Pi County in Sichuan Province. Chinese Geographical Science, 20 (2), 176–183. doi:10.1007/s11769-010-0176-z
   Web of Science ®Google Scholar
• Wang, X.H., Yu, S., and Huang, G.H., 2004. Land allocation based on integrated GIS-optimization modeling at a watershed level. Landscape and Urban Planning, 66 (2), 61–74. doi:10.1016/S0169-2046(03)00095-1
   Web of Science ®Google Scholar
• Watson, P.M. and Wadsworth, R.A., 1996. A computerised decision support system for rural policy formulation. International Journal of Geographical Information Systems, 10 (4), 425–440. doi:10.1080/02693799608902088
   Web of Science ®Google Scholar
• Wu, D., et al., 2010. Simulating urban expansion by coupling a stochastic cellular automata model and socioeconomic indicators. Stochastic Environmental Research and Risk Assessment : Research Journal, 24 (2), 235–245. doi:10.1007/s00477-009-0313-3
   Web of Science ®Google Scholar
• Xiang, W.N. and Kaiser, E.J., 1992. Conflict prediction and prevention in rural land use planning: a GIS approach. Progress in Rural Policy and Planning, 1 (2), 34–37.
   Google Scholar
• Xu, Q.L., et al., 2014. Agent-based modeling and simulations of land-use and land-cover change according to ant colony optimization: a case study of the Erhai Lake Basin, China. Natural Hazards, doi:10.1007/s11069-014-1303-4
   Web of Science ®Google Scholar
• Xu, Y. and Tang, Q., 2009. Land use optimization at small watershed scale on the Loess Plateau. Journal Geographic Sciences, 19, 577–586. doi:10.1007/s11442-009-0577-z
   Web of Science ®Google Scholar
• Zhang, W., 2002. Modeling and solving a resource allocation problem with soft constraint techniques ( Tech. Rep., WUCS-2002-13). St. Louis, MO: Department of Computer Science and Engineering, Washington University.
   Google Scholar
• Zhang, X.L., Wu, Y.Z., and Shen, L.Y., 2011. An evaluation framework for the sustainability of urban land use: a study of capital cities and municipalities in China. Habitat International, 35 (1), 141–149. doi:10.1016/j.habitatint.2010.06.006
   Web of Science ®Google Scholar
• Zhang, Y., et al., 2012. Agricultural land use optimal allocation system in developing area: application to Yili watershed, Xinjiang Region. Chinese Geographical Science, 22 (2), 232–244. doi:10.1007/s11769-012-0530-4
   Web of Science ®Google Scholar