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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

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

Heuristic algorithms;Fitness function;Constraints;Effectiveness;Efficiency;Spatial optimization;Spatial planning

Acknowledgement

Supported by : 한국환경산업기술원

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