Accuracy and validity assessment of application algorithms in land use allocation into comparison LP, SA, MOLA and MDCHOICE

References

• Aerts JCJH, Eisinger E, Heuvelink GBM, Stewart TJ. 2003. Using linear integer programming for multi‐site land‐use allocation. Geogr Anal. 35(2):148–169.
   Web of Science ®Google Scholar
• Aerts JCJH, Heuvelink GBM. 2002. Using simulated annealing for resource allocation. Int J Geogr Inf Sci. 16(6):571–587.
   Web of Science ®Google Scholar
• Amini-Hosseini K. 2012. An earthquake risk–sensitive model for spatial land-use allocation. 15th World Conference on Earthquake Engineering (15WCEE). Lisbon (PT).
   Google Scholar
• Amiri M, Salmanmahiny A, Jalai S, Hosseini Seyed Mohen F, Dehkordi A. 2009. Comparison of maps integration system approach and combination of fuzzy_bolean logic in ecological land use planning in 33 and 34 watersheds of north Iran. Environ Sci. 7(2):109–123.
   Google Scholar
• Beyer HL, Dujardin Y, Watts ME, Possingham HP. 2016. Solving conservation planning problems with integer linear programming. Ecol Modell. 328:14–22.
   Web of Science ®Google Scholar
• Bourque K, Schiller A, Loyola Angosto C, McPhail L, Bagnasco W, Ayres A, Larsen A. 2019. Balancing agricultural production, groundwater management, and biodiversity goals: a multi-benefit optimization model of agriculture in Kern County, California. Sci Total Environ. 670:865–875.
   PubMed Web of Science ®Google Scholar
• Chakrabarty D. 2008. Algorithmic aspects of connectivity, allocation and design problems (Thesis). Georgia, Georgia Institute of Technology. https://search.proquest.com/docview/304644438?accountid=188395.
   Google Scholar
• Chen Y, Li X, Liu X, Liu Y. 2010. An agent-based model for optimal land allocation (AgentLA) with a contiguity constraint. Int J Geogr Inf Sci. 24(8):1269–1288.
   Web of Science ®Google Scholar
• Chuvieco E. 1993. Integration of linear programming and GIS for land-use modelling. Int J Geogr Inf Sci. 7(1):71–83.
   Web of Science ®Google Scholar
• Cisty M. 2010. Hybrid genetic algorithm and linear programming method for least-cost design of water distribution systems. Water Resour Manage. 24(1):1–24.
   Web of Science ®Google Scholar
• Clark WV, Hosking PL. 1986 Statistical methods for geographers. New York (NY): Wiley.
   Google Scholar
• Datta D, Deb K, Fonseca CM, Lobo F, Condado P, Seixas J. 2007. Multi-objective evolutionary algorithm for land-use management problem. Int J Comput Intell Res. 3(4):371–384.
   Google Scholar
• Deng JS, Wang K, Hong Y, Qi JG. 2009. Spatio-temporal dynamics and evolution of land use change and landscape pattern in response to rapid urbanization. Landscape Urban Plann. 92(3–4):187–198.
   Web of Science ®Google Scholar
• Dezhkam S, Jabbarian AB, Darvishsefat AA, Sakieh Y. 2017. Performance evaluation of land change simulation models using landscape metrics. Geocarto Int. 32(6):655–677.
   Web of Science ®Google Scholar
• Eastman JR. 2012. IDRISI Selva tutorial. Idrisi Production. Clark Labs-Clark Univ. 45(January):51–63.
   Google Scholar
• French AP, Robinson AC, Wilson JM. 2001. Using a hybrid genetic-algorithm/branch and bound approach to solve feasibility and optimization integer programming problems. J Heuristics. 7(6):551–564.
   Web of Science ®Google Scholar
• Fu B-J, Zhao W-W, Chen L-D, Liu Z-F, Lü Y-H. 2005. Eco-hydrological effects of landscape pattern change. Landscape Ecol Eng. 1(1):25–32.
   Google Scholar
• Gabriel SA, Faria JA, Moglen GE. 2006. A multiobjective optimization approach to smart growth in land development. Socio-Econ Plan Sci. 40(3):212–248.
   Google Scholar
• Gao P, Wang H, Cushman SA, Cheng C, Song C, Ye S. 2021. Sustainable land-use optimization using NSGA-II : theoretical and experimental comparisons of improved algorithms. Landscape Ecol. 36(7):1877–1892.
   Web of Science ®Google Scholar
• Genova K, Guliashki V. 2011. Linear integer programming methods and approaches–a survey. J Cybern Inf Technol. 11(1):1–23.
   Google Scholar
• Gorsevski PV, Jankowski P. 2010. An optimized solution of multi-criteria evaluation analysis of landslide susceptibility using fuzzy sets and Kalman filter. Comput Geosci. 36(8):1005–1020.
   Web of Science ®Google Scholar
• Hagen A. 2002. Multi-method assessment of map similarity. 5th AGILE Conference on Geographic Information Science, Palma. 1–8.
   Google Scholar
• Hagen A. 2003. Fuzzy set approach to assessing similarity of categorical maps. Int J Geogr Inf Sci. 17(3):235–249.
   Web of Science ®Google Scholar
• Hajehforooshnia S, Soffianian A, Mahiny AS, Fakheran S. 2011. Multi objective land allocation (MOLA) for zoning Ghamishloo Wildlife Sanctuary in Iran. J Nat Conserv. 19(4):254–262.
   Web of Science ®Google Scholar
• Herold M, Scepan J, Clarke KC. 2002. The use of remote sensing and landscape metrics to describe structures and changes in urban land uses. Environ Plan A. 34(8):1443–1458.
   Web of Science ®Google Scholar
• Jahanishakib F, Ardakani T, Salmanmahiny A. 2018. Comparison and validation of land use optimization approaches using new algorithms in the city of Gorgan. Geographical Planning of Space. Internet. 8(29):47–60.
   Google Scholar
• Kabodi A, Salmanmahini A, Mirkarim S. 2012. Enhancing MOLA based on economic, social and strategic values (case study: Golestan Province) (Thesis). Gorgan, Gorgan University of agricultural sciences and natural resources.
   Google Scholar
• Kamyab H, Salmanmahini A, Sharaiini M. 2015. Enhancing MOLA in terms of landscape metrics and Genetic Algorithm. Land Use Planning. 7(1):29–47.
   Google Scholar
• Kirkpatrick S, Gelatt CD, Vecchi MP. 1983. Optimization by simulated annealing. Science. 220(4598):671–680.
   PubMed Web of Science ®Google Scholar
• Kontos T, Katikas L. 2019. Delimiting future urban sprawl boundaries using a GIS-based Model for ecological sensitivity index assessment and optimization techniques. The case of mytilene (Lesvos Island, Greece). Eur J Sustain Dev Res. 3(1):em0074.
   Google Scholar
• Kumar P. 2014. Mathematical programming approaches for land use planning that limits urban sprawl (Thesis). United States, University of Texas at Arlington.
   Google Scholar
• Kumar P, Rosenberger JM, Iqbal GMD. 2016. Mixed integer linear programming approaches for land use planning that limit urban sprawl. Comput Ind Eng. 102:33–43.
   Web of Science ®Google Scholar
• Ligmann‐Zielinska A, Church RL, Jankowski P. 2008. Spatial optimization as a generative technique for sustainable multiobjective land‐use allocation. Int J Geogr Inf Sci. 22(6):601–622.
   Web of Science ®Google Scholar
• Lubida A, Veysipanah M, Pilesjo P, Mansourian A. 2019. Land-use planning for sustainable urban development in Africa: a spatial and multi-objective optimization approach. Geod Cartogr. 45(5):1–15.
   Google Scholar
• Malczewski J. 2004. GIS-based land-use suitability analysis: a critical overview. Prog Plann. 62(1):3–65.
   Web of Science ®Google Scholar
• Matthews KB, Craw S, Elder S, Sibbald AR, MacKenzie I. 2000. Applying genetic algorithms to multi-objective land use planning. Genetic Evolutionary Computation Conference (GECCO 2000). Morgan Kaufmann. 613–620.
   Google Scholar
• Mirzaii M, Riyahi A, Salmanmahini A, Gholamalifard M. 2013. Change evaluating of land cove by landscape metrics in Mazandaran Province betwean 1984-2000. Appl Ecol J. 2(4):37–55.
   Google Scholar
• Mohammadi M, Nastaran M, Sahebgharani A. 2016. Development, application, and comparison of hybrid meta-heuristics for urban land-use allocation optimization: tabu search, genetic, GRASP, and simulated annealing algorithms. Comput Environ Urban Syst. 60:23–36.
   Web of Science ®Google Scholar
• Nath SS, Bolte JP, Ross LG, Aguilar-Manjarrez J. 2000. Applications of geographical information systems (GIS) for spatial decision support in aquaculture. Aquacult Eng. 23(1–3):233–278.
   Web of Science ®Google Scholar
• Nyarko EK, Cupec R, Filko D. 2014. A Comparison of several heuristic algorithms for solving high dimensional optimization Problems. Int J Electric Comput Eng Syst. 5(1):1–8.
   Google Scholar
• Pahlavani P, Sheikhian H, Bigdeli B. 2020. Evaluation of residential land use compatibilities using a density-based IOWA operator and an ANFIS-based model: a case study of Tehran, Iran. Land Use Policy. 90(March):104364.
   Web of Science ®Google Scholar
• Paritosh P, Kalita B, Sharma D. 2019. A game theory based land layout optimization of cities using genetic algorithm. Int J Manage Sci Eng Manage. 14(3):155–168.
   Google Scholar
• Podnar H, Skorin-Kapov J. 2003. Genetic algorithm for network cost minimization using threshold based discounting. J Appl Math Decision Sci. 7(4):207–228.
   Google Scholar
• Podnar H, Skorin-Kapov J, Skorin-Kapov D. 2002. Network cost minimization using threshold-based discounting. Eur J Oper Res. 137(2):371–386.
   Web of Science ®Google Scholar
• Pontius RG. 2000. Quantification error versus location error in comparison of categorical maps. Photogramm Eng Remote Sens. 66(8):1011–1016.
   Web of Science ®Google Scholar
• Pontius RG Jr., Millones M. 2008. Problems and solutions for kappa-based indices of agreement. Studying, Modeling and Sense Making of Planet Earth, Mytilene, Greece.
   Google Scholar
• Pontius RG Jr., Schneider LC. 2001. Land-cover change model validation by and {ROC} method for the {Ipswitch Watershed, Massachusetts, USA}. Agric Ecosyst Environ. 85(1–3):239–248.
   Web of Science ®Google Scholar
• Pontius RG, Walker R, Yao-Kumah R, Arima E, Aldrich S, Caldas M, Vergara D. 2007. Accuracy assessment for a simulation model of Amazonian deforestation. Ann Assoc Am Geogr. 97(4):677–695.
   Web of Science ®Google Scholar
• Power C, Simms A, White R. 2001. Hierarchical fuzzy pattern matching for the regional comparison of land use maps. Int J Geogr Inf Sci. 15(1):77–100.
   Web of Science ®Google Scholar
• Rahman MM, Szabó G. 2021. Multi-objective urban land use optimization using spatial data: a systematic review. Sustain Cities Soc. 74:103214.
   Web of Science ®Google Scholar
• Raidl GR, Puchinger J. 2008. Combining (integer) linear programming techniques and metaheuristics for combinatorial optimization. In: Blum C, Aguilera MJB., Roli A., Sampels M. editors, Hybrid Metaheuristics. Studies in Computational Intelligence, vol. 114. Berlin: Springer; p. 31–62.
   Google Scholar
• SaeedSabaee M, Salmanmahiny A, Shahraeini S, Mirkarimi SH, Dabiri N. 2016. Application of a hybrid linear programming and ant colony metaheuristic with aid of GIS to land use allocation. Iran J Remote Sens GIS. 8(1):109–126.
   Google Scholar
• SaeedSabaee M, Salmanmahiny A, Shahraeini S, Mirkarimi SH, Dabiri N. 2018. Application of CPLEX solver to land use optimization in Gorgan township. J Environ Stud. 44(3):445–459.
   Google Scholar
• Sahebgharani A. 2016. Multi-objective land use optimization through parallel particle swarm algorithm: case study Baboldasht district of Isfahan, Iran. J Urb Environ Engng. 10(1):42–49.
   Google Scholar
• Sakieh Y, Salmanmahiny A. 2016. Performance assessment of geospatial simulation models of land-use change-a landscape metric-based approach. Environ Monit Assess. 188(3):169.
   PubMed Web of Science ®Google Scholar
• Salmanmahiny A, Asadolahi Z, SaiedSabaee M, Kamyab H, NasirAhmadi K. 2014. A comparison of simulated annealing (SA) and multi objective land allocation (MOLA) for solving the problem of multi-objective land allocation. Iran J Appl Ecol. 3(9):1–13.
   Google Scholar
• Salmanmahiny A, Kamyab H. 2009. Applaied remote sensing and GIS with Idrisi. 2nd ed. Press Mehr Mahdis, Tehran.
   Google Scholar
• Santé-Riveira I, Boullón-Magán M, Crecente-Maseda R, Miranda-Barrós D. 2008. Algorithm based on simulated annealing for land-use allocation. Comput Geosci. 34(3):259–268.
   Web of Science ®Google Scholar
• Schwaab J, Deb K, Goodman E, Lautenbach S, van Strien MJ, Grêt-Regamey A. 2018. Improving the performance of genetic algorithms for land-use allocation problems. Int J Geogr Inf Sci. 32(5):907–930.
   Web of Science ®Google Scholar
• Seixas J, Nunes JP, Lourenço P, Corte-Real J. 2007. GeneticLand: modelling land-use change using evolutionary algorithms. In: Modelling land-use change. Dordrecht: Springer; p.181–197.
   Google Scholar
• Sharma SK, Lees BG. 2004. A comparison of simulated annealing and Gis based Molafor solving the problem of multi-objective land use assessment and allocation. Proceedings of the 17th International Conference on Multiple Criteria Decision Analysis. Whistler, Canada.
   Google Scholar
• Sharmin N, Haque A, Islam MM. 2019. Generating alternative land-use allocation for mixed use areas: multi-objective optimization approach. Geogr Anal. 51(4):448–474.
   Web of Science ®Google Scholar
• Shayegan M, Alimohamadi A, Mansorian A. 2012. Multi-objective optimization approach for land use allocation based on NSGA-II. Iranian Remote Sens GIS. 4(2):1–18.
   Google Scholar
• Song M, Chen D. 2018a. An improved knowledge-informed NSGA-II for multi-objective land allocation (MOLA). Geo-Spatial Inf Sci. 21(4):273–287.
   Google Scholar
• Song M, Chen D. 2018b. A comparison of three heuristic optimization algorithms for solving the multi-objective land allocation (MOLA) problem. Ann GIS. 24(1):19–31.
   Web of Science ®Google Scholar
• Stewart TJ, Janssen R, van Herwijnen M. 2004. A genetic algorithm approach to multiobjective land use planning. Comput Oper Res. 31(14):2293–2313.
   Web of Science ®Google Scholar
• Türk E, Zwick PD. 2019. Optimization of land use decisions using binary integer programming: the case of Hillsborough County, Florida, USA. J Environ Manage. 235(January):240–249.
   PubMed Web of Science ®Google Scholar
• Williams JC. 2002. A zero‐one programming model for contiguous land acquisition. Geogr Anal. 34(4):330–349.
   Web of Science ®Google Scholar
• Xiaoli L, Chen Y, Daoliang L. 2009. A spatial decision support system for land-use structure optimization. WSEAS Trans Comput. 8(3):439–448.
   Google Scholar