Advertisement

A Performance Evaluation and Two New Implementations of Evolutionary Algorithms for Land Partitioning Problem

  • Huseyin HakliEmail author
Research Article - Special Issue - Intelligent Computing And Interdisciplinary Applications
  • 25 Downloads

Abstract

Many bio-inspired techniques are proposed and implemented to solve real-world applications. The number of these techniques is increasing day by day, so the researchers (especially out of computer sciences) have difficulty in deciding which technique to select for the problem. In this study, two new implementations to solve land partitioning problem and also a performance analysis of three evolutionary algorithms were carried out on this real-world engineering problem. Land partitioning is a discrete optimization problem that cannot be solved in linear time with conventional techniques. Two new implementations of automated land partitioning (ALP-DE and ALP-SS) were carried out by using differential evolution algorithm (DE) and scatter search (SS) methods. The algorithms were adapted to the land partitioning problem by being discretized with permutation coding. These two proposed methods were compared with a similar study in the published literature and a designer’s plan for a project area that contains 18 blocks using a mathematical model. These proposed automatic methods (ALP-DE and ALP-SS) resulted in more successful and more appropriate partitioning plans than those of a designer in accordance with land partitioning criteria. When the comparison of these three different evolutionary algorithms was examined, the ALP-SS method showed superior performance in all blocks. The low standard deviation values of the proposed methods indicated that both methods are robust and successful tools for the land partitioning problem.

Keywords

Evolutionary computation Differential evolution algorithm Scatter search Automated land partitioning Discrete optimization Performance evaluation 

References

  1. 1.
    Hakli, H.; Uguz, H.: A novel approach for automated land partitioning using genetic algorithm. Expert Syst. Appl. 82, 10–18 (2017).  https://doi.org/10.1016/j.eswa.2017.03.067 CrossRefGoogle Scholar
  2. 2.
    Latruffe, L.; Piet, L.: Does land fragmentation affect farm performance? A case study from Brittany, France. Agric. Syst. 129, 68–80 (2014).  https://doi.org/10.1016/j.agsy.2014.05.005 CrossRefGoogle Scholar
  3. 3.
    Dijk van, T.: Central European land fragmentation in the years to come—a scenario study into the future need for land consolidation in central Europé FIG XXII international congress. Washington, D.C. (2002).Google Scholar
  4. 4.
    Demetriou, D.; Stillwell, J.; See, L.: Land consolidation in Cyprus: Why is an integrated planning and decision support system required? Land Use Policy 29(1), 131–142 (2012).  https://doi.org/10.1016/j.landusepol.2011.05.012 CrossRefGoogle Scholar
  5. 5.
    Vitikainen, A.: An overview of land consolidation in Europe. Nordic J. Surv. Real Estate Res. 1, 15–34 (2004)Google Scholar
  6. 6.
    Cay, T.; Iscan, F.: Fuzzy expert system for land reallocation in land consolidation. Expert Syst. Appl. 38(9), 11055–11071 (2011).  https://doi.org/10.1016/j.eswa.2011.02.150 CrossRefGoogle Scholar
  7. 7.
    Cay, T., Iscan, F.: Optimization in land consolidation. Paper presented at the XXIII FIG Congress, Munich, GermanyGoogle Scholar
  8. 8.
    Avci, M.: A new approach oriented to new reallotment model based on block priority method in land consolidation Tr. J. Agric. For 23, 451–457 (1999)Google Scholar
  9. 9.
    Ayranci, Y.: Re-allocation aspects in land consolidation: a new model and its applications. J. Agron. 6(2), 270–277 (2007)CrossRefGoogle Scholar
  10. 10.
    Ertunc, E.; Cay, T.; Hakli, H.: Modeling of reallocation in land consolidation with a hybrid method. Land Use Policy 76, 754–761 (2018).  https://doi.org/10.1016/j.landusepol.2018.03.003 CrossRefGoogle Scholar
  11. 11.
    Demetriou, D.; Stillwell, J.; See, L.: An integrated planning and decision support system (IPDSS) for land consolidation: theoretical framework and application of the land-redistribution modules. Environ. Plan. B 39(4), 609–628 (2012).  https://doi.org/10.1068/b37075 CrossRefGoogle Scholar
  12. 12.
    Aslan, S.T.A.; Kirmikil, M.; Giindogdu, K.S.; Arici, I.: Reallocation model for land consolidation based on landowners’ requests. Land Use Policy 70, 463–470 (2018).  https://doi.org/10.1016/j.landusepol.2017.11.028 CrossRefGoogle Scholar
  13. 13.
    Uguz, H., Hakli, H.: A new land redistribution model using discrete artificial bee colony algorithm. Paper presented at the 2nd international conference on science, ecology and technology (ICONSETE), Barcelona, Spain, 14–16 OctoberGoogle Scholar
  14. 14.
    Hakli, H.; Uguz, H.; Cay, T.: Genetic algorithm supported by expert system to solve land redistribution problem. Expert Syst. 35(6), e12308 (2018).  https://doi.org/10.1111/exsy.12308 CrossRefGoogle Scholar
  15. 15.
    Hakli, H.; Uguz, H.; Cay, T.: A new approach for automating land partitioning using binary search and Delaunay triangulation. Comput. Electron. Agric. 125, 129–136 (2016)CrossRefGoogle Scholar
  16. 16.
    Buis, A.M.; Vingerhoeds, R.A.: Knowledge-based systems in the design of a new parcelling. Knowl. Based Syst. 9(5), 307–314 (1996).  https://doi.org/10.1016/0950-7051(96)01044-1 CrossRefGoogle Scholar
  17. 17.
    Rosman, F.: Automated parcel boundary design systems in land consolidation. Paper presented at the FIG Working Week 2012, Rome, Italy, 6–10 MayGoogle Scholar
  18. 18.
    Tourino, J.; Parapar, J.; Doallo, R.; Boullon, M.; Rivera, F.F.; Bruguera, J.D.; Gonzalez, X.P.; Crecente, R.; Alvarez, C.: A GIS-embedded system to support land consolidation plans in Galicia. Int. J. Geogr. Inf. Sci. 17(4), 377–396 (2003).  https://doi.org/10.1080/1365881031000072636 CrossRefGoogle Scholar
  19. 19.
    Demetriou, D.; See, L.; Stillwell, J.: A spatial genetic algorithm for automating land partitioning. Int. J. Geogr. Inf. Sci. 27(12), 2391–2409 (2013)CrossRefGoogle Scholar
  20. 20.
    Dahal, K.R.; Chow, T.E.: A GIS toolset for automated partitioning of urban lands. Environ. Modell. Softw. 55, 222–234 (2014).  https://doi.org/10.1016/j.envsoft.2014.01.024 CrossRefGoogle Scholar
  21. 21.
    Wickramasuriya, R.; Chisholm, L.A.; Puotinen, M.; Gill, N.; Klepeis, P.: An automated land subdivision tool for urban and regional planning: concepts, implementation and testing. Environ. Modell. Softw. 26(12), 1675–1684 (2011).  https://doi.org/10.1016/j.envsoft.2011.06.003 CrossRefGoogle Scholar
  22. 22.
    Kucukmehmetoglu, M.; Geymen, A.: Optimization models for urban land readjustment practices in Turkey. Habitat Int. 53, 517–533 (2016).  https://doi.org/10.1016/j.habitatint.2015.12.020 CrossRefGoogle Scholar
  23. 23.
    Wickramasuriya, R.; Chisholm, L.A.; Puotinen, M.; Gill, N.; Klepeis, P.: A method to dynamically subdivide parcels in land use change models. Int. J. Geogr. Inf. Sci. 27(8), 1497–1513 (2013).  https://doi.org/10.1080/13658816.2012.752491 CrossRefGoogle Scholar
  24. 24.
    Chen, W.; Panahi, M.; Pourghasemi, H.R.: Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. CATENA 157, 310–324 (2017)CrossRefGoogle Scholar
  25. 25.
    Yeguas-Bolivar, E.; Munoz-Salinas, R.; Medina-Carnicer, R.; Carmona-Poyato, A.: Comparing evolutionary algorithms and particle filters for Markerless Human Motion Capture. Appl. Soft Comput. 17, 153–166 (2014)CrossRefGoogle Scholar
  26. 26.
    Cruz-Aceves, I.; Hernandez-Aguirre, A.; Valdez, S.I.: On the performance of nature inspired algorithms for the automatic segmentation of coronary arteries using Gaussian matched filters. Appl. Soft Comput. 46, 665–676 (2016)CrossRefGoogle Scholar
  27. 27.
    Kumari, A.C.; Srinivas, K.: Comparing the performance of quantum-inspired evolutionary algorithms for the solution of software requirements selection problem. Inf. Softw. Tech. 76, 31–64 (2016)CrossRefGoogle Scholar
  28. 28.
    Piotrowski, A.P.; Napiorkowski, M.J.; Napiorkowski, J.J.; Rowinski, P.M.: Swarm Intelligence and Evolutionary Algorithms: performance versus speed. Inf. Sci. 384, 34–85 (2017).  https://doi.org/10.1016/j.ins.2016.12.028 MathSciNetCrossRefGoogle Scholar
  29. 29.
    Pan, Q.K.; Wang, L.; Gao, L.; Li, W.D.: An effective hybrid discrete differential evolution algorithm for the flow shop scheduling with intermediate buffers. Inf. Sci. 181(3), 668–685 (2011).  https://doi.org/10.1016/j.ins.2010.10.009 CrossRefGoogle Scholar
  30. 30.
    Marti, R.; Laguna, M.; Glover, F.: Principles of scatter search. Eur. J. Oper. Res. 169(2), 359–372 (2006).  https://doi.org/10.1016/j.ejor.2004.08.004 MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Storn, R., Price, K.: Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces. In: Technical Report TR-95-012. International Computer Science Institute, Berkeley (1995)Google Scholar
  32. 32.
    Chaves-Gonzalez, J.M.; Vega-Rodriguez, M.A.: DNA strand generation for DNA computing by using a multi-objective differential evolution algorithm. Biosystems 116, 49–64 (2014)CrossRefGoogle Scholar
  33. 33.
    Glotic, A.; Glotic, A.; Kitak, P.; Pihler, J.; Ticar, I.: Optimization of hydro energy storage plants by using differential evolution algorithm. Energy 77, 97–107 (2014).  https://doi.org/10.1016/j.energy.2014.05.004 CrossRefGoogle Scholar
  34. 34.
    Sethanan, K.; Pitakaso, R.: Differential evolution algorithms for scheduling raw milk transportation. Comput. Electron. Agric. 121, 245–259 (2016).  https://doi.org/10.1016/j.compag.2015.12.021 CrossRefGoogle Scholar
  35. 35.
    Shih, M.Y.; Enriquez, A.C.; Hsiao, T.Y.; Trevino, L.M.T.: Enhanced differential evolution algorithm for coordination of directional overcurrent relays. Electr. Power Syst. Res. 143, 365–375 (2017)CrossRefGoogle Scholar
  36. 36.
    Glover, F.: Heuristics for integer programming using surrogate constraint. Decis. Sci. 8, 156–166 (1977)CrossRefGoogle Scholar
  37. 37.
    Glover, F.: A template for scatter search and path relinking. Artif. Evol. 1363, 3–51 (1998)Google Scholar
  38. 38.
    Laguna, M.; Martí, R.; Gallego, M.; Duarte, A.: The Scatter Search Methodology. Wiley Encyclopedia of Operations Research and Management ScienceWiley, Hoboken (2011).  https://doi.org/10.1002/9780470400531.eorms0284 CrossRefGoogle Scholar
  39. 39.
    Tang, J.F.; Zhang, J.; Pan, Z.D.: A scatter search algorithm for solving vehicle routing problem with loading cost. Expert Syst. Appl. 37(6), 4073–4083 (2010).  https://doi.org/10.1016/j.eswa.2009.11.027 CrossRefGoogle Scholar
  40. 40.
    Duman, E.; Ozcelik, M.H.: Detecting credit card fraud by genetic algorithm and scatter search. Expert Syst. Appl. 38(10), 13057–13063 (2011).  https://doi.org/10.1016/j.eswa.2011.04.110 CrossRefGoogle Scholar
  41. 41.
    Naderi, B.; Ruiz, R.: A scatter search algorithm for the distributed permutation flowshop scheduling problem. Eur. J. Oper. Res. 239(2), 323–334 (2014).  https://doi.org/10.1016/j.ejor.2014.05.024 MathSciNetCrossRefzbMATHGoogle Scholar
  42. 42.
    Padua, S.G.B.; Cossi, A.M.; Mantovani, J.R.S.: Planning of medium-voltage electric power distribution systems through a scatter search algorithm. IEEE Latin Am. Trans. 13(8), 2637–2645 (2015)CrossRefGoogle Scholar
  43. 43.
    Kitayama, S.; Arakawa, M.; Yamazaki, K.: Discrete differential evolution for mixed discrete non-linear problems. J. Civ. Eng. Archit. 6(5), 594–605 (2012)Google Scholar
  44. 44.
    Uyan, M.; Cay, T.; Akcakaya, O.: A spatial decision support system design for land reallocation: a case study in Turkey. Comput. Electron. Agric. 98, 8–16 (2013).  https://doi.org/10.1016/j.compag.2013.07.010 CrossRefGoogle Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2019

Authors and Affiliations

  1. 1.Department of Computer EngineeringNecmettin Erbakan UniversityKonyaTurkey

Personalised recommendations