Skip to main content

Advertisement

SpringerLink
Log in

Evolutionary Multi-objective Optimization for landscape system design

  • Original Article
  • Published:
Journal of Geographical Systems Aims and scope Submit manuscript

Abstract

Increasing recognition of the extent and speed of habitat fragmentation and loss, particularly in the urban fringe, is driving the need to analyze qualitatively and quantitatively regional landscape structures in land-use planning and environmental policy implementation. This paper introduces an Evolutionary Multi-objective Optimization (EMO) methodology to estimate the Pareto optimal set of landscape designs generated from a series of underlying ecological principles. The results of applying these principles via EMO to a study site are presented and a hierarchical clustering methodology is introduced to assist in evaluating the population of solutions generated.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Aarts EHL, Lenstra JK (1997) Introduction. In: Aarts EHL, Lenstra JK (eds) Local search in combinatorial optimization. John Wiley & Sons Ltd., New York, pp 1–18

    Google Scholar 

  • Albrecht J, Ehlers M (1994) Virtual geographic information system VGIS. In: Nievergelt J, Roos T, Schek H, Widmayer P (eds) IGIS’94: Geographic information systems. International workshop on advanced research in geographic information systems, Ascona. Springer Lecture Notes in Computer Science, vol 884. Springer, Berlin, pp 55–58

  • Beyer HG (2001) The theory of evolution strategies. Natural computing series. Springer, Berlin

    Google Scholar 

  • Chakhar S, Mousseau V (2007) An algebra for multicriteria spatial modeling. Comput Environ Urban Syst 31:572–596

    Article  Google Scholar 

  • Chakhar S, Mousseau V (2008) GIS-based multicriteria spatial modeling generic framework. Int J Geogr Inf Sci 22(11):1159–1196

    Article  Google Scholar 

  • Chartrand G, Lesniak L (1996) Graphs and digraphs, 3rd edn. Chapman and Hall, London

    Google Scholar 

  • Chen Y, Li X, Su W, Li Y (2008) Simulating the optimal land-use pattern in the farming-pastoral transitional zone of northern China. Comput Environ Urban Syst 32:407–414

    Article  Google Scholar 

  • Cliff AD, Ord JK (1973) Spatial autocorrelation. Pion Ltd., London

    Google Scholar 

  • Dasgupta D, Michalewicz Z (eds) (1997) Evolutionary algorithms in engineering applications. Springer, Berlin

    Google Scholar 

  • Deb K, Goel T (2001) Controlled elitist Non-dominated Sorting Genetic Algorithms for better convergence. In: Zitzler E et al (eds) Proceedings of the first international conference, evolutionary multi-criterion optimization, Springer, Zurich. Lecture Notes in Computer Science, vol 1993, pp 67–81

  • Deb K, Goldberg DE (1989) An investigation of niche and species formation in genetic function optimization. In: Schaffer JD (ed) Proceedings of the third international conference on genetic algorithms, Morgan Kaufmann, pp 42–50

  • Deb K, Pratap A, Agarak S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 8(2):182–197

    Article  Google Scholar 

  • Dramstad WE, Olson JD, Forman RTT (1996) Landscape ecology principles in landscape architecture and land-use planning. Harvard Graduate School of Design, Island Press, American Society of Landscape Architects, Washington, DC

    Google Scholar 

  • Ducheyne EI, Wulf RRD, Baets BD (2006) A spatial approach to forest-management optimization: linking GIS and multiple objective genetic algorithms. Int J Geogr Inf Sci 20(8):917–928

    Article  Google Scholar 

  • Duh JD, Brown DG (2007) Knowledge-informed Pareto simulated annealing for multi-objective spatial allocation. Comput Environ Urban Syst 31:253–281

    Article  Google Scholar 

  • Falkenauer E (1998) Genetic algorithms and grouping problems. Wiley, New York

    Google Scholar 

  • Forman RTT (1990) Ecologically sustainable landscapes: the role of spatial configuration. In: Zonneveld IS, Forman RTT (eds) Changing landscapes: an ecological perspective. Springer, Berlin, pp 261–278

    Google Scholar 

  • Forman RTT (1997) Land mosaics: the ecology of landscapes and regions. Cambridge University Press, Cambridge, UK

    Google Scholar 

  • Groot JCJ, Rossing WAH, Jellema A, van Ittersum MK (2006) Landscape design and agricultural land-use allocation using Pareto-based multi-objective differential evolution. In: Proceedings of the third biannual meeting of the international environmental modelling and software society, International Environmental Modelling and Software Society, Burlington, VT, http://www.iemss.org/iemss2006/sessions/all.html

  • Groot JCJ, Rossing WAH, Jellema A, van Ittersum MK (2007) Exploring multi-scale trade-offs between nature conservation, agricultural profits and landscape quality—a methodology to support discussions on land-use perspectives. Agric Ecosyst Environ 120(1):58–69

    Article  Google Scholar 

  • Huang B, Fery P, Xue L, Wang Y (2008) Seeking the Pareto front for multiobjective spatial optimization problems. Int J Geogr Inf Sci 22(5):507–526

    Article  Google Scholar 

  • Janssen R, van Herwijnen M, Stewart TJ, Aerts JCJH (2008) Multiobjective decision support for land-use planning. Environ Plan B 35:740–756

    Article  Google Scholar 

  • Jiang B, Omer I (2007) Spatial topology and its structural analysis based on the concept of simplicial complex. Trans GIS 11(6):943–960

    Article  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

    Article  Google Scholar 

  • Loonen W, Heuberger PSC, Bakema AH, Schot P (2007) Improving the spatial coherence of nature using genetic algorithms. Environ Plan B 34:369–378

    Article  Google Scholar 

  • Manson SM (2006) Bounded rationality in agent-based models: experiments with evolutionary programs. Int J Geogr Inf Sci 20(9):991–1012

    Article  Google Scholar 

  • Marray L, Lau M, Russell L (1997) Credit Valley Natural Heritage Project: detailed methodology. Tech. Rep. Credit Valley Conservation

  • Matisziw TC, Murray AT (2006) Promoting species persistence through spatial association optimization in nature reserve design. J Geogr Syst 8:289–305

    Article  Google Scholar 

  • Mazumder P, Rudnick EM (eds) (1999) Genetic algorithms for VLSI design, layout and test automation. Prentice-Hall PTR, Upper Saddle River, NJ

    Google Scholar 

  • McGarigal K, Marks BJ (1995) FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. Tech. Rep. PNW-GTR-351. United States Department of Agriculture, Pacific Northwest Research Station, Portland, OR

  • McIntosh RH (1976) Ecology since 1900. In: Taylor BJ, White TJ (eds) Issues and ideas in America. University of Oklahoma Press, Norman, OK, pp 353–372

    Google Scholar 

  • Mitchell M (1996) An introduction to genetic algorithms. The MIT Press, Cambridge, MA

    Google Scholar 

  • Molenaar M (1998) An introduction to the theory of spatial object modelling for GIS. Research monographs in GIS. Taylor & Francis, London

    Google Scholar 

  • Mooney P, Winstanley A (2006) An evolutionary algorithm for multicriteria path optimization problems. Int J Geogr Inf Sci 20(4):401–423

    Article  Google Scholar 

  • Moulton CM, Roberts SA, Calamai PH (2009) Hierarchical clustering of multiobjective optimization results to inform land use decision making. Urban Reg Inf Syst J 21(2):25–37

    Google Scholar 

  • Province of Ontario (1989) Wetlands policy statement: a draft policy for consultation under section 3 of the planning act. Tech. Rep. Province of Ontario

  • Quagliarella D, Périaux J, Poloni C, Winter G (eds) (1998) Genetic algorithms and evolution strategies in engineering and computer science: recent advances and industrial applications. Wiley, New York

    Google Scholar 

  • Rahmat-Samii Y, Michielssen E (eds) (1999) Electromagnetic optimization by genetic algorithms. Wiley series in microwave and optical engineering. Wiley, New York

    Google Scholar 

  • Rardin RL (1998) Optimization in operations research. Prentice-Hall, Upper Saddle River, NJ

    Google Scholar 

  • Roberts SA (2003) Configuration optimization in socio-ecological systems. Unpublished Ph.D. thesis. Department of Systems Design Engineering, University of Waterloo, http://proquest.umi.com/pqdweb?did=7650422061&sid=3&Fmt=2&clientId=27850&RQT=309&VName=PQD&cfc=1

  • Saarloos DJM, Arentze TA, Borgers AWJ, Timmermans HJP (2008) A multi-agent paradigm as structuring principle for planning support systems. Comput Environ Urban Syst 32:29–40

    Google Scholar 

  • Srinivas N, Deb K (1995) Multiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2(3):221–248

    Article  Google Scholar 

  • Wu X, Murray AT (2008) A new approach to quantifying spatial contiguity using graph theory and spatial interaction. Int J Geogr Inf Sci 22(4):387–407

    Article  Google Scholar 

  • Xiao N (2008) A unified conceptual framework for geographical optimization using evolutionary algorithms. Ann Assoc Am Geogr 98(4):795–817

    Article  Google Scholar 

  • Xiao N, Bennett DA, Armstrong MP (2007) Interactive evolutionary approaches to multiobjective spatial decision making: a synthetic review. Comput Environ Urban Syst 31:232–252

    Article  Google Scholar 

  • Zhang X, Armstrong MP (2008) Genetic algorithms and the corridor location problem: multiple objectives and alternate solutions. Environ Plan B 35:148–168

    Article  Google Scholar 

  • Zitzler E, Deb K, Thiele L, Coello CAC, Corne D (eds) (2001) Evolutionary multi-criterion optimization: first international conference, EMO 2001. Lecture Notes in Computer Science, vol 1993, Springer, Zurich

Download references

Acknowledgments

The authors would like to acknowledge partial support of the this work from GEOIDE-NCE Grant HSS-SDS-17. Thanks also to Christina Moulton for help in creating the results data set and to the editors and reviewers for helpful suggestions on the presentation of our ideas and results.

Author information

Authors and Affiliations

  1. Department of Geography and Environmental Studies, Wilfrid Laurier University, Waterloo, ON, Canada

    S. A. Roberts

  2. School of Surveying, University of Otago, Dunedin, New Zealand

    G. B. Hall

  3. Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada

    P. H. Calamai

Authors
  1. S. A. Roberts
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. B. Hall
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. H. Calamai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. A. Roberts.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Roberts, S.A., Hall, G.B. & Calamai, P.H. Evolutionary Multi-objective Optimization for landscape system design. J Geogr Syst 13, 299–326 (2011). https://doi.org/10.1007/s10109-010-0136-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10109-010-0136-2

Keywords

JEL Classification

Search

Navigation