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Using Evolutionary Algorithms to Generate Alternatives for Multiobjective Site-Search Problems

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  • Ningchuan Xiao
  • David A Bennett
  • Marc P Armstrong

Abstract

Multiobjective site-search problems are a class of decision problems that have geographical components and multiple, often conflicting, objectives; this kind of problem is often encountered and is technically difficult to solve. In this paper we describe an evolutionary algorithm (EA) based approach that can be used to address such problems. We first describe the general design of EAs that can be used to generate alternatives that are optimal or close to optimal with respect to multiple criteria. Then we define the problem addressed in this research and discuss how the EA was designed to solve it. In this procedure, called MOEA/Site, a solution (that is, a site) is encoded by using a graph representation that is operated on by a set of specifically designed evolutionary operations. This approach is applied to five different types of cost surfaces and the results are compared with 10 000 randomly generated solutions. The results demonstrate the robustness and effectiveness of this EA-based approach to geographical analysis and multiobjective decisionmaking. Critical issues regarding the representation of spatial solutions and associated evolutionary operations are also discussed.

Suggested Citation

  • Ningchuan Xiao & David A Bennett & Marc P Armstrong, 2002. "Using Evolutionary Algorithms to Generate Alternatives for Multiobjective Site-Search Problems," Environment and Planning A, , vol. 34(4), pages 639-656, April.
    • Handle: RePEc:sae:envira:v:34:y:2002:i:4:p:639-656
    DOI: 10.1068/a34109
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. Xingdong Zhang & Marc P Armstrong, 2008. "Genetic Algorithms and the Corridor Location Problem: Multiple Objectives and Alternative Solutions," Environment and Planning B, , vol. 35(1), pages 148-168, February.
    3. Inés Santé Riveira & Rafael Crecente Maseda, 2006. "A Review of Rural Land-Use Planning Models," Environment and Planning B, , vol. 33(2), pages 165-183, April.
    4. Burcin Bozkaya & Seda Yanik & Selim Balcisoy, 2010. "A GIS-Based Optimization Framework for Competitive Multi-Facility Location-Routing Problem," Networks and Spatial Economics, Springer, vol. 10(3), pages 297-320, September.
    5. Saeid Hamzeh & Marzieh Mokarram & Azadeh Haratian & Harm Bartholomeus & Arend Ligtenberg & Arnold K. Bregt, 2016. "Feature Selection as a Time and Cost-Saving Approach for Land Suitability Classification (Case Study of Shavur Plain, Iran)," Agriculture, MDPI, vol. 6(4), pages 1-13, October.
    6. Gopalan, Ram & Hachadoorian, Lee & Kimbrough, Steven O. & Murphy, Frederic H., 2024. "Selecting good redistricting plans from a large pool of available plans using the efficient frontier," Omega, Elsevier, vol. 124(C).
    7. Rongrong Li & Yee Leung & Hui Lin & Bo Huang, 2013. "An adaptive compromise programming method for multi-objective path optimization," Journal of Geographical Systems, Springer, vol. 15(2), pages 211-228, April.
    8. Ming Zhao & Qiuwen Chen, 2015. "Risk-based optimization of emergency rescue facilities locations for large-scale environmental accidents to improve urban public safety," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(1), pages 163-189, January.
    9. Xiaolan Wu & Tony Grubesic, 2010. "Identifying irregularly shaped crime hot-spots using a multiobjective evolutionary algorithm," Journal of Geographical Systems, Springer, vol. 12(4), pages 409-433, December.
    10. Liu, Xiaoping & Ou, Jinpei & Li, Xia & Ai, Bin, 2013. "Combining system dynamics and hybrid particle swarm optimization for land use allocation," Ecological Modelling, Elsevier, vol. 257(C), pages 11-24.
    11. Kamyoung Kim & Alan T Murray & Ningchuan Xiao, 2008. "A Multiobjective Evolutionary Algorithm for Surveillance Sensor Placement," Environment and Planning B, , vol. 35(5), pages 935-948, October.

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