Journal of the Korean Society of Environmental Restoration Technology (한국환경복원기술학회지)

The Korea Society of Environmental Restoration Technology (한국환경복원기술학회)
권호 표지
DOI QR코드

DOI QR Code

Basic Study on Spatial Optimization Model for Sustainability using Genetic Algorithm - Based on Literature Review -

유전알고리즘을 이용한 지속가능 공간최적화 모델 기초연구 - 선행연구 분석을 중심으로 -

  • Yoon, Eun-Joo (Interdisciplinary Program in Landscape Architecture, Seoul National University) ;
  • Lee, Dong-Kun (Dept. of Landscape Architecture and Rural System Engineering, Seoul National University)
  • 윤은주 (서울대학교 협동과정 조경학) ;
  • 이동근 (서울대학교 조경지역시스템공학부)
  • Received : 2017.12.08
  • Accepted : 2017.12.28
  • Published : 2017.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

As cities face increasing problems such as aging, environmental pollution and growth limits, we have been trying to incorporate sustainability into urban planning and related policies. However, it is very difficult to generate a 'sustainable spatial plans' because there are trade-offs among environmental, society, and economic values. This is a kind of non-linear problem, and has limitations to be solved by existing qualitative expert knowledge. Many researches from abroad have used the meta heuristic optimization algorithms such as Genetic Algorithms(GAs), Simulated Annealing(SA), Ant Colony Optimization(ACO) and so on to synthesize competing values in spaces. GAs is the most frequently applied theory and have been known to produce 'good-enough plans' in a reasonable time. Therefore we collected the research on 'spatial optimization model based GAs' and analyzed in terms of 'study area', 'optimization objective', 'fitness function', and 'effectiveness/efficiency'. We expect the results of this study can suggest that 'what problems the spatial optimization model can be applied to' and 'linkage possibility with existing planning methodology'.

Keywords

References

  1. Kim, M. J. 2015. Multiobjective optimization problems to redistricting plans. The geographical journal of Korea 49(2) : 173-185. (In Korean)
  2. Park, Y. S..D. K. Lee.E. J. Yoon.Y. W. Mo and J. H. Leem. 2017. Land Use Optimization Using Genetic Algorithm-Focused on Yangpyeong-eup-. J. Environ. Impact Assess 26(1) : 44-67. (In Korean) https://doi.org/10.14249/eia.2017.26.1.44
  3. Jung, E. J..B. H. Jeong and J. M. Na. 2016. A study on the sustainability and resilience of city. Journal of The Korean Regional Development Association 28(4) : 87-108. (In Korean)
  4. Ahmadi, A..M. F. Karamouz.A. Moridi and D. Han. 2012. Integrated planning of land use and water allocation on a watershed scale considering social and water quality issues. J Water Resour Plan Manag 138 : 671-681. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000212
  5. Beheshtifar, S. and A. Alimoahmmadi. 2015. A multiobjective optimization approach for location-allocation of clinics. Int Trans Oper Res 22 : 313-328. https://doi.org/10.1111/itor.12088
  6. Cao, K..M. Batty.B. Huang.Y. Liu.L. Yu and J. Chen. 2011. Spatial multi-objective land use optimization: extensions to the non-dominated sorting genetic algorithm-II. Int J Geogr Inf Sci 25 : 1949-1969. https://doi.org/10.1080/13658816.2011.570269
  7. Cao, K..B. Huang.S. Wang and H. Lin. 2012. Sustainable land use optimization using Boundary-based Fast Genetic Algorithm. Comput Environ Urban Syst 36 : 257-269. https://doi.org/10.1016/j.compenvurbsys.2011.08.001
  8. Cao, K. and X. Ye. 2013. Coarse-grained parallel genetic algorithm applied to a vector based land use allocation optimization problem: The case study of Tongzhou Newtown, Beijing, China. Stoch Environ Res Risk Assess 27 : 1133-1142. https://doi.org/10.1007/s00477-012-0649-y
  9. Caparros-Midwood, D. and S. B. R. Dawson. 2015. Optimised spatial planning to meet long term urban sustainability objectives. Computers, Environment and Urban Systems 54 : 154-164. https://doi.org/10.1016/j.compenvurbsys.2015.08.003
  10. Chen, H. W. and N. Chang. 2006. Decision support for allocation of watershed pollution load using grey fuzzy multi objective programming. J Am Water Resour Assoc 42 : 725-745. https://doi.org/10.1111/j.1752-1688.2006.tb04488.x
  11. Chen, Y..X. Li.X. Liu and Y. Liu. 2010. An agent-based model for optimal land allocation (AgentLA) with a contiguity constraint. Int J Geogr Inf Sci 24 : 1269-1288. https://doi.org/10.1080/13658810903401024
  12. Chen, W..G. J. Carsjens.L. Zhao and H. Li. 2014. A spatial optimization model for sustainable land use at regional level in China: A case study for Poyang lake region. Sustainability 7 : 35-55. https://doi.org/10.3390/su7010035
  13. Datta, D..C. M. Fonseca and K. Deb. 2008. A multi-objective evolutionary algorithm to exploit the similarities of resource allocation problems. J Sched 11 : 405-419. https://doi.org/10.1007/s10951-008-0073-9
  14. Ding, X. W..Z. Y. Shen.Q. Hong.Z. F. Yang.X. Wu and R. M. Liu. 2010. Development and test of the Export Coefficient Model in the Upper Reach of The Yangtze River. J Hydrol 383 : 233-244. https://doi.org/10.1016/j.jhydrol.2009.12.039
  15. Duh, J. and D. G. Brown. 2007. Knowledge-informed Pareto simulated annealing for multi-objective spatial allocation. Comput Environ Urban Syst 31 : 253-281. https://doi.org/10.1016/j.compenvurbsys.2006.08.002
  16. Eikelboom, T..R. Janssen and T. J. Stewart. 2015. A spatial optimization algorithm for geodesign. Landsc Urban Plan 144 : 10-21. https://doi.org/10.1016/j.landurbplan.2015.08.011
  17. Eldrandaly, K. 2010. A GEP-based spatial decision support system for multisite land use allocation. Appl Soft Comput J 10 : 694-702. https://doi.org/10.1016/j.asoc.2009.07.014
  18. Fotakis, D..E. Sidiropoulos. 2014. Combined land-use and water allocation planning. Ann Oper Res 219(1) : 169-185. https://doi.org/10.1007/s10479-012-1080-y
  19. Giacomoni, M. H..J. Joseph. 2017. Multi-objective evolutionary optimization and monte carlo simulation for placement of Low Impact Development in the Catchment Scale. J.Water Resour. Plann. Manage 143(9) : 04017053. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000812
  20. Ghahraman, B. and A. R. Sepaskhah. 2004. Linear and nonlinear optimization models for allocation of a limited water supply. J. Irrig. Drain. Eng 124 : 138-149.
  21. Gong, J..Y. Liu and W. Chen. 2012. Optimal land use allocation of urban fringe in Guangzhou.. J Geogr Sci 22 : 179-191. https://doi.org/10.1007/s11442-012-0920-7
  22. Haque, A..Y. Asami. 2014. Optimizing urban land use allocation for planners and real estate developers. Comput Environ Urban Syst 46 : 57-69. https://doi.org/10.1016/j.compenvurbsys.2014.04.004
  23. Holzkamper, A. and R. Seppelt. 2007. A generic tool for optimising land-use patterns and landscape structures. Environment Modelling & Software 22 : 1801-1804. https://doi.org/10.1016/j.envsoft.2007.02.008
  24. Huang, B. and W. Zhang. 2012. Sustainable land-use planning for a downtown lake area in central China: Multiobjective optimization approach aided by urban growth modeling. J. Urban Plann. Dev 140(2).
  25. Jankowski, P..G. Fraley and E. Pebesma. 2014. An exploratory approach to spatial decision support. Comput Environ Urban Syst 45 : 101-113. https://doi.org/10.1016/j.compenvurbsys.2014.02.008
  26. Karakostas, S. M. 2017. Bridging the gap between multi-objective optimization and spatial planning: a new post-processing methodology capturing the optimum allocation of land uses against established transportation infrastructure. Transp Plan Technol 40 : 305-326. https://doi.org/10.1080/03081060.2017.1283157
  27. Karamouz, M..A. Ahmadi and S. Nazif. 2009. Development of management schemes in irrigation planning: Economic and crop pattern consideration. Trans A Civ Eng 16 : 457-466.
  28. Karamouz, M..B. Zahraie.R. Kerachian.A. Eslami. 2010. Crop pattern and conjunctive use management: A case study. Irrig Drain 59 : 161-173.
  29. Li, X. and L. Parrott. 2016. An improved Genetic Algorithm for spatial optimization of multi-objective and multi-site land use allocation. Comput Environ Urban Syst 59 : 184-194. https://doi.org/10.1016/j.compenvurbsys.2016.07.002
  30. Ligmann-Zielinska, A..R. Church and P. Jankowski. 2008. Spatial optimization as a generative technique for sustainable multiobjective land-use allocation. Int. J. Geogr. Inf. Sci 22 : 601-622. https://doi.org/10.1080/13658810701587495
  31. Liu, Y..W. Tang.J. He.Y. Liu.T. Ai and D. Liu. 2015a. A land-use spatial optimization model based on genetic optimization and game theory. Comput Environ Urban Syst 49 : 1-14. https://doi.org/10.1016/j.compenvurbsys.2014.09.002
  32. Liu, Y..M. Yuan and J. He. 2015b. Regional land-use allocation with a spatially explicit genetic algorithm. Landscape Ecol Eng 11 : 209-219. https://doi.org/10.1007/s11355-014-0267-6
  33. Mansfield, E. and G. W. Yohe. 1988. Microeconomics: Theory, applications. New York: Norton.
  34. Matthews, K. B..S. Craw.S. Elder.A. R. Sibbald and I. MacKenzie. 2000. Applying Genetic Algorithms to multi-objective land use planning. Proceedings of Genetic and Evolutionary Computation Conference (GE CCO 2000)
  35. Matthews, K. B..K. Buchan.A. R. Sibbald and S. Craw. 2006. Combining deliberative and computer-based methods for multi-objective land-use planning. Agric Syst 87 : 18-37. https://doi.org/10.1016/j.agsy.2004.11.002
  36. Mays, L. W. 1989. Reliability analysis of water distribution systems, ASCE, Reston, VA.
  37. Mi, N..J. Hou.W. Mi and N. Song. 2015. Optimal spatial land-use allocation for limited development ecological zones based on the geographic information system and a genetic ant colony algorithm. Int J Geogr Inf Sci 29 : 2174-2193. https://doi.org/10.1080/13658816.2015.1070411
  38. Mohammadi, M..M. Nastaran and A. Sahebgharani. 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. https://doi.org/10.1016/j.compenvurbsys.2016.07.009
  39. Morio, M..S. Schadler and M. Finkel. 2013. Applying a multi-criteria genetic algorithm framework for brownfield reuse optimization: Improving redevelopment options based on stakeholder preferences. J Environ Manage 130 : 331-346. https://doi.org/10.1016/j.jenvman.2013.09.002
  40. Neema, M. N. and A. Ohgai. 2010. Multi-objective location modeling of urban parks and open spaces: Continuous optimization. Comput Environ Urban Syst 34 : 359-376. https://doi.org/10.1016/j.compenvurbsys.2010.03.001
  41. Panagopoulos, Y..C. Makropoulos and M. Mimikou. 2014. Decision support for agricultural water management. Glob Nest J 14 : 255-263.
  42. Parolo, G..A. Ferrarini and G. Rossi. 2009. Optimization of tourism impacts within protected areas by means of genetic algorithms, Ecol Modell 220 : 1138-1147. https://doi.org/10.1016/j.ecolmodel.2009.01.012
  43. Porta, J..J. Parapar.R. Doallo.F. F. Rivera.I. Sante and R. Crecente. 2013. High performance genetic algorithm for land use planning. Comput Environ Urban Syst 37 : 45-58. https://doi.org/10.1016/j.compenvurbsys.2012.05.003
  44. Roberts, S. A..G. B. Hall and P. H. Calamai. 2011. Evolutionary Multi-objective Optimization for landscape system design. J Geogr Syst 13 : 299-326. https://doi.org/10.1007/s10109-010-0136-2
  45. Shaygan, M..A. Alimohammadi.A. Mansourian.Z.S. Govara and S.M. Kalami. 2014. Spatial multi-objective optimization approach for land use allocation using NSGA-II. IEEE J Sel Top Appl Earth Obs Remote Sens 7 : 873-883.
  46. Stewart, T. J..R. Janssen and M. Van Herwijnen. 2004. A genetic algorithm approach to multiobjective land use planning. Comput Oper Res 31 : 2293-2313. https://doi.org/10.1016/S0305-0548(03)00188-6
  47. Stewart, T. J. and R. Janssen. 2014. A multiobjective GIS-based land use planning algorithm. Comput Environ Urban Syst 46 : 25-34. https://doi.org/10.1016/j.compenvurbsys.2014.04.002
  48. Wang, F. and Q. Tang. 2013. Planning toward equal accessibility to services: A quadratic programming approach. Environment and Planning B: Planning and Design 40 : 195-212. https://doi.org/10.1068/b37096
  49. Weber, R. P. 1990. Basic content analysis, Sage.
  50. Yazdi, J..S. A. A. alehi Neyshabouri.M. H. Niksokhan.S. Sheshangosht and M. Elmi. 2013. Optimal prioritisation of watershed management measures for flood risk mitigation on a watershed scale. J Flood Risk Manag 6 : 372-384. https://doi.org/10.1111/jfr3.12016
  51. Yim, K. K. W..S. C. Wong.A. Chen.C. K. Wong and W. H. K. Lam. 2011. A reliability-based land use and transportation optimization model. Transp Res Part C Emerg Technol 19 : 351-362. https://doi.org/10.1016/j.trc.2010.05.019
  52. Yuan, M..Y. Liu.J. He and D. Liu. 2014. Regional land-use allocation using a coupled MAS and GA model: from local simulation to global optimization, a case study in Caidian District, Wuhan, China. Cartogr Geogr Inf Sci 41 : 363-378. https://doi.org/10.1080/15230406.2014.931251
  53. Zhang, H. H..Y. N. Zeng and L. Bian. 2010. Simulating multi-objective spatial optimization allocation of land use based on the integration of multi-agent system and genetic algorithm. Int J Environ Res 4 : 765-776.
  54. Zhang, W..H. Wang.F. Han.J. Gao.T. Nguyen and Y. Chen. 2014. Modeling urban growth by the use of a multiobjective optimization approach : Environmental and economic issues for the Yangtze watershed, China. Environ Sci Pollut Res 21 : 13027-13042. https://doi.org/10.1007/s11356-014-3007-4
  55. Zhang, W. and B. Huang. 2015. Soil erosion evaluation in a rapidly urbanizing city (Shenzhen, China) and implementation of spatial land-use optimization. Environ Sci Pollut Res 22 : 4475-4490. https://doi.org/10.1007/s11356-014-3454-y
  56. Zhang, W..K. Cao.S. Liu and B. Huang. 2016. A multi-objective optimization approach for health-care facility location-allocation problems in highly developed cities such as Hong Kong. Comput Environ Urban Syst 59 : 220-230. https://doi.org/10.1016/j.compenvurbsys.2016.07.001
  57. Zhao, L. and Z.R. Peng. 2010. Integrated Bilevel Model to Explore Interaction Between Land Use Allocation and Transportation. Transp Res Rec J 2176 : 14-25. https://doi.org/10.3141/2176-02
  58. Web of Science: www.webofscience.com