Skip to main content
Log in

A Multiobjective Hybrid Metaheuristic Approach for GIS-based Spatial Zoning Model

  • Published:
Journal of Mathematical Modelling and Algorithms

Abstract

This paper presents a multiobjective hybrid metaheuristic approach for an intelligent spatial zoning model in order to draw territory line for geographical or spatial zone for the purpose of space control. The model employs a Geographic Information System (GIS) and uses multiobjective combinatorial optimization techniques as its components. The proposed hybrid metaheuristic consists of the symbiosis between tabu search and scatter search method and it is used heuristically to generate non-dominated alternatives. The approach works with a set of current solution, which through manipulation of weights are optimized towards the non-dominated frontier while at the same time, seek to disperse over the frontier by a strategic oscillation concept. The general procedure and its algorithms are given as well as its implementation in the GIS environment. The computation has resulted in tremendous improvements in spatial zoning.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Agnew, J.: Geography and Regional Science Program: Geographic Approaches to Democratization, National Science Foundation, Washington, 1994.

    Google Scholar 

  2. Altman, M.: Redistricting principles and democratic representation, Ph.D. Thesis, California Institute of Technology, Pasadena, CA, 1998.

  3. Bong, C. W. and Wang, Y. C.: An intelligent GIS-based spatial zoning with multiobjective hybrid metaheuristic method, in Proceeding of the 17th International Conference on Industrial & Engineering Applications of Artificial Intelligence and Expert System(IEA/EIA 2004), University of Otawa, Canada, 2004.

  4. Bozkaya, B., Erkut, E. and Laporte, G.: A tabu search heuristic and adaptive memory procedure for political districting, European J. Oper. Res.144 (2003), 12–26.

    Google Scholar 

  5. Church, R. L. and Sorensen, P.: Integrating normative location models into GIS: Problems and prospects with the p-median model, Technical Report 94–5, NCGIA, National Center for Geographic Information and Analysis, 1994.

  6. Clarke, G. P. and Wilson, A. G.: Performance indicators in urban planning: The historical context, in C. S. Bertuglia, G. P. Clarke and A. G. Wilson (eds), Modeling the City: Performance, Policy and Planning, Routedge, New York, 1994, Chapter 4.

    Google Scholar 

  7. Current, J., Daskin, M. and Schilling, D.: Discrete network location models, in Z. Drezner and H. Hamacher (eds), Facility Location Theory: Applications and Methods, 2001, Chapter 3.

  8. Ehrgott, M. and Gandibleux, X.: A survey and annotated bibliography of multiobjective combinatorial optimisation, in OR Spektrum22, Springer-Verlag, 2000, pp. 425–460.

    Google Scholar 

  9. Glover, F., Laguna, M. and Martí, R.: Fundamentals of scatter search and path relinking, Control and Cybernetics39(3) (2000), 653–684.

    Google Scholar 

  10. Grabaum, R. and Meyer, B. C.: Multicriteria optimization of landscapes using GIS-based functional assessments, Landscape and Urban Planning43(1-3) (1998), 21–34.

    Google Scholar 

  11. Hansen, M. P.: Tabu search for multiobjective optimization: MOTS, in Proceedings of MCDM'97, 1997.

  12. Jain, A. S.: A multi-level hybrid framework for the deterministic job-shop scheduling problem, Ph.D. Thesis, Department of Applied Physics, Electronic & Mechanical Engineering University of Dundee, Dundee, Scotland, U.K., 1998.

  13. Jaszkiewicz, A.: Multiple objective metaheuristic algorithms for combinatorial optimization (Draft), Habilitation thesis, 360, Poznan University of Technology, Poznan, 2001.

    Google Scholar 

  14. Knowles, J. D.: Local-search and hybrid evolutional algorithms for Pareto optimization, Ph.D. Thesis, Department of Computer Science, University of Reading, Reading, U.K., 2002.

    Google Scholar 

  15. Landa-Silva, J. D. and Burke, E. K.: Multiobjective metaheuristics for scheduling and timetabling, University of Nottingham, U.K., 2002 [Online Tutorial]; URL: http://tew.ruca.ua.ac.be/eume/welcome.htm?workshops/momh/

    Google Scholar 

  16. Malczewski, J.: GIS and Multicriteria Decision Analysis, Wiley, New York, 1999.

    Google Scholar 

  17. Martin, D.: Automated zone design in GIS, in P. Atkinson and D. Martin (eds), GIS and Geocomputation, Innovation in GIS 7, Taylor & Francis, London, 2000, Chapter 15.

    Google Scholar 

  18. Openshaw, S.: Developing GIS-relevant zone-based spatial analysis methods, in P. Longley and M. Batty (eds), Spatial Analysis:Modeling in a GIS Environment, GeoInformation Intenational, Cambridge, 1996, Chapter 4.

  19. Wang, Y. C. and Bong, C. W.: Compactness measurement using fuzzy multicriteria decision making for redistricting, in Proceeding IEEE REGION 10 International Conference on Electrical and Electronic Technology, IEEE Press, 2001.

  20. Yagiura, M. and Ibaraki, T.: On metaheuristic algorithms for combinatorial optimization problems, The Transactions of the Institute of Electronics, Information and Communication EngineersJ83-D-I(1) (2000), 3–25.

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wei, B.C., Chai, W.Y. A Multiobjective Hybrid Metaheuristic Approach for GIS-based Spatial Zoning Model. Journal of Mathematical Modelling and Algorithms 3, 245–261 (2004). https://doi.org/10.1023/B:JMMA.0000038615.32559.af

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/B:JMMA.0000038615.32559.af

Navigation