A bat-inspired approach to define transition rules for a cellular automaton model used to simulate urban expansion

International Journal of Geographical Information Science

Volumn 30, Issue 10, 2016, Pages 1961-1979

  Cao, Min ^a   Bennett, Sean J ^b   Shen, Quanfei ^c   Xu, Ruqi ^a  

^a NANJING NORMAL UNIVERSITY   (China)

^b UNIVERSITY AT BUFFALO   (United States)

^c Provincial Fundamental Geomatic Center of Jiangsu   (China)

Author keywords

Bat algorithm; cellular automata; swarm intelligence; transition rules; urban expansion

Indexed keywords

ALGORITHM; ARTIFICIAL INTELLIGENCE; CELLULAR AUTOMATON; MODEL VALIDATION; OPTIMIZATION; URBAN DEVELOPMENT; URBANIZATION;

CHINA; JIANGSU; NANJING [JIANGSU];

EID: 84964005031     PISSN: 13658816     EISSN: 13623087     Source Type: Journal    
DOI: 10.1080/13658816.2016.1151521     Document Type: Article

References (56)

1. C.M.Almeida,, et al., 2003. Stochastic cellular automata modeling of urban land use dynamics: Empirical development and estimation. Computers, Environment and Urban Systems, 27, 481–509. doi:10.1016/S0198-9715(02)00042-X.
2. C.M.Almeida,, et al., 2008. Using neural networks and cellular automata for modelling intra-urban land-use dynamics. International Journal of Geographical Information Science, 22 (9), 943–963. doi:10.1080/13658810701731168.
3. M.Batty,, Y.C.Xie,, and Z.L.Sun,, 1999. Modeling urban dynamics through GIS-based cellular automata. Computers, Environment and Urban Systems, 23, 205–233. doi:10.1016/S0198-9715(99)00015-0.
4. M.Cao,, et al., 2013. A cellular automata model for simulating the evolution of positive-negative terrains in a small loess watershed. International Journal of Geographical Information Science, 27 (7), 1349–1363. doi:10.1080/13658816.2012.756882.
5. M.Cao,, et al., 2015. A new discovery of transition rules for cellular automata by using cuckoo search algorithm. International Journal of Geographical Information Science, 29 (5), 806–824. doi:10.1080/13658816.2014.999245.
6. P.Civicioglu, and E.Besdok,, 2013. A conceptual comparison of the cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artificial Intelligence Review, 39, 315–346. doi:10.1007/s10462-011-9276-0.
7. K.C.Clarke, and L.J.Gaydos,, 1998. Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore. International Journal of Geographical Information Science, 12 (7), 699–714. doi:10.1080/136588198241617.
8. K.C.Clarke,, S.Hoppen,, and L.J.Gaydos,, 1997. A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B: Planning and Design, 24, 247–261. doi:10.1068/b240247.
9. H.Couclelis,, 1997. From cellular automata to urban models: New principles for model development and implementation. Environment and Planning B: Planning and Design, 24, 165–174. doi:10.1068/b240165.
10. Y.J.Feng,, et al., 2011. Modeling dynamic urban growth using cellular automata and particle swarm optimization rules. Landscape and Urban Planning, 102, 188–196. doi:10.1016/j.landurbplan.2011.04.004.
11. Y.J.Feng, and Y.Liu,, 2013. A heuristic cellular automata approach for modelling urban land-use change based on simulated annealing. International Journal of Geographical Information Science, 27 (3), 449–466. doi:10.1080/13658816.2012.695377.
12. J.E.Fieldsend,, 2009. Optimizing decision trees using multi-objective particle swarm optimization. In: C.A.Coello Coello, et al., eds. Swarm intelligence for multi-objective problems in data mining. Vol. 242. Berlin: Springer, 93–114.
13. C.He,, et al., 2008. Modelling dynamic urban expansion processes incorporating a potential model with cellular automata. Landscape and Urban Planning, 86 (1), 79–91. doi:10.1016/j.landurbplan.2007.12.010.
14. K.E.Heraguemi,, et al., 2014. Association rule mining based on bat algorithm. In: L.Pan, et al., Eds. BIC-TA 2014, CIS 472. Wuhan: Springer, 182–186.
15. B.Huang,, et al., 2009. Land-use-change modelling using unbalanced support-vector machines. Environment and Planning B: Planning and Design, 36, 398–416. doi:10.1068/b33047.
16. G.D.Jenerette, and J.Wu,, 2001. Analysis and simulation of land-use change in the central Arizona–Phoenix region, USA. Landscape Ecology, 16, 611–626. doi:10.1023/A:1013170528551.
17. M.Jiang,, Y.P.Luo,, and S.Y.Yang,, 2007. Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm. Information Processing Letters, 102, 8–16. doi:10.1016/j.ipl.2006.10.005.
18. I.Karafyllidis,, 2004. Design of a dedicated parallel processor for the prediction of forest fire spreading using cellular automata and genetic algorithms. Engineering Applications of Artificial Intelligence, 17 (1), 19–36. doi:10.1016/j.engappai.2003.12.001.
19. K.Khan,, A.Nikov,, and A.Sahai,, 2011. A fuzzy bat clustering method for ergonomic screening of office workplaces, S3T 2011. Advances in Intelligent and Soft Computing, 101, 59–66.
20. K.Khan, and A.Sahai,, 2012. A fuzzy c-means bi-sonar-based metaheuristic optimization algorithm. International Journal of Interactive Multimedia and Artificial Intelligence, 1 (7), 26–32. doi:10.9781/ijimai.2012.173.
21. G.Komarasamy, and A.Wahi,, 2012. An optimized K-means clustering technique using bat algorithm. European Journal of Scientific Research, 84 (2), 263–273.
22. X.Li, and A.G.Yeh,, 2002. Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science, 16 (4), 323–343. doi:10.1080/13658810210137004.
23. X.Li, and A.G.O.Yeh,, 2004. Data mining of cellular automata’s transition rules. International Journal of Geographical Information Science, 18, 723–744. doi:10.1080/13658810410001705325.
24. X.C.Li, and X.P.Liu,, 2014. A systematic sensitivity analysis of constraint cellular automata model for urban growth simulation based on different transition rules. International Journal of Geographical Information Science, 28 (7), 1317–1335. doi:10.1080/13658816.2014.883079.
25. J.F.Liao,, et al., 2014. A neighbor decay cellular automata approach for simulating urban expansion based on particle swarm intelligence. International Journal of Geographical Information Science, 28 (4), 720–738. doi:10.1080/13658816.2013.869820.
26. J.H.Lin,, et al., 2012. A chaotic Levy flight bat algorithm for parameter estimation in nonlinear dynamic biological systems. Journal of Computer and Information Technology, 2 (2), 56–63.
27. X.P.Liu,, et al., 2008a. A bottom-up approach to discover transition rules of cellular automata using ant intelligence. International Journal of Geographical Information Science, 5, 37–41.
28. X.P.Liu,, et al., 2008b. Simulating complex urban development using kernel-based non-linear cellular automata. Ecological Modelling, 211, 169–181. doi:10.1016/j.ecolmodel.2007.08.024.
29. X.P.Liu,, X.Li,, and A.G.O.Yeh,, 2007. Discovery of transition rules for geographical cellular automata by using ant colony optimization. Science in China (Series D), 50 (10), 1578–1588. doi:10.1007/s11430-007-0083-z.
30. X.B.Meng,, et al., 2015. A novel bat algorithm with habitat selection and Doppler Effect in echoes for optimization. Expert Systems with Applications, 42, 6350–6364. doi:10.1016/j.eswa.2015.04.026.
31. P.Musikapun, and P.Pongcharoen,, 2012. Solving multi-stage multi-machine multi-product scheduling problem using bat aglorithm. 2nd International Conference on Management and Artificial Intelligence (IPEDR), Vol. 35. Singapore: IACSIT Press, 98–102.
32. G.R.Pontius, and J.Malanson,, 2005. Comparison of the structure and accuracy of two land change models. International Journal of Geographical Information Science, 19, 243–265. doi:10.1080/13658810410001713434.
33. G.R.Pontius, and M.Millones,, 2011. Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32 (15), 4407–4429. doi:10.1080/01431161.2011.552923.
34. B.Ramesh,, V.C.J.Mohan,, and V.C.V.Reddy,, 2013. Application of bat algorithm for combined economic load and emission dispatch. International Journal of Electrical Engineering and Telecommunications, 2 (1), 1–9.
35. A.Rienow, and R.Goetzke,, 2015. Supporting SLEUTH – Enhancing a cellular automaton with support vector machines for urban growth modeling. Computers, Environment and Urban Systems, 49, 66–81. doi:10.1016/j.compenvurbsys.2014.05.001.
36. A.Rienow, and D.Stenger,, 2014. Geosimulation of urban growth and demographic decline in the Ruhr: a case study for 2025 using the artificial intelligence of cells and agents. Journal of Geographical Systems, 16 (3), 311–342. doi:10.1007/s10109-014-0196-9.
37. L.Rokach, and O.Maimon,, 2002. Top-down induction of decision trees classifiers – A survey. IEEE Transactions on Systems, Man and Cybernetics: Part C, 1 (11), 1–12.
38. P.S.Shelokar,, V.K.Jayaraman,, and B.D.Kulkarni,, 2004. An ant colony classifier system: application to some process engineering problems. Computers and Chemical Engineering, 28, 1577–1584. doi:10.1016/j.compchemeng.2003.12.004.
39. M.A.M.Shukran,, et al., 2011. Artificial bee colony based data mining algorithms for classification tasks. Modern Applied Science, 5, 217–231.
40. M.Sood, and S.Bansal,, 2013. K-medoids clustering technique using bat algorithm. International Journal of Applied Information Systems, 5 (8), 20–22. doi:10.5120/ijca.
41. D.P.Ward,, A.T.Murray,, and S.R.Phinn,, 2000. A stochastically constrained cellular model of urban growth. Computers, Environment and Urban Systems, 24, 539–558. doi:10.1016/S0198-9715(00)00008-9.
42. R.White, and G.Engelen,, 1993. Cellular automata and fractal urban form: a cellular modelling approach to the evolution of urban land-use patterns. Environment and Planning A, 25, 1175–1199. doi:10.1068/a251175.
43. R.White,, G.Engelen,, and I.Uijee,, 1997. The use of constrained cellular automata for high-resolution modelling of urban land use dynamics. Environment and Planning B: Planning and Design, 24, 323–343. doi:10.1068/b240323.
44. F.Wu,, 2002. Calibration of stochastic cellular automata: The application to rural urban land conversions. International Journal of Geographical Information Science, 16 (8), 795–818. doi:10.1080/13658810210157769.
45. F.Wu, and C.J.Webster,, 1998. Simulation of land development through the integration of cellular automata and multicriteria evaluation. Environment and Planning B: Planning and Design, 25, 103–126. doi:10.1068/b250103.
46. X.S.Yang,, et al., 2013a. Swarm intelligence and bio-inspired computation: theory and applications. 1st ed. London: Elsevier, 3–20.
47. J.Y.Yang,, et al., 2013b. An intelligent method to discover transition rules for cellular automata using bee colony optimisation. International Journal of Geographical Information Science, 27, 1849–1864. doi:10.1080/13658816.2013.823498.
48. J.Y.Yang,, 2014. Research on discovering transition rules of geographic cellular automata based on bee colony optimization. Published Master thesis. Nanjing Normal University. (in Chinese)
49. X.S.Yang,, 2010. A new metaheuristic bat-inspired algorithm. In: J.R. Gonzalez et al. eds. Nature inspired cooperative strategies for optimization (NICSO 2010). Berlin: Springer, 65–74.
50. X.S.Yang,, 2011. Bat algorithm for multi-objective optimisation. International Journal of Bio-inspired Computation, 3 (5), 267–274. doi:10.1504/IJBIC.2011.042259.
51. X.S.Yang,, 2014. Swarm intelligence based algorithms: a critical analysis. Evolutionary Intelligence, 7, 17–28. doi:10.1007/s12065-013-0102-2.
52. X.S.Yang, and A.H.Gandomi,, 2012. Bat algorithm: a novel approach for global engineering optimization. Engineering Computations, 29 (5), 464–483. doi:10.1108/02644401211235834.
53. X.S.Yang, and X.He,, 2013. Bat algorithm: literature review and applications. International Journal of Bio-Inspired Computation, 5 (3), 141–149. doi:10.1504/IJBIC.2013.055093.
54. X.S.Yang,, M.Karamanoglu,, and S.Fong,, 2012. Bat aglorithm for topology optimization in microelectronic applications, In: IEEE Int. Conference on Future Generation Communication Technology (FGCT2012), British Computer Society, 12–14Dec, 2012. London, 150–155.
55. A.G.O.Yeh, and X.Li,, 2006. Errors and uncertainties in urban cellular automata. Computers Environment and Urban Systems, 30, 10–28. doi:10.1016/j.compenvurbsys.2004.05.007.
56. J.W.Zhang, and G.G.Wang,, 2012. Image matching using a bat algorithm with mutation. Applied Mechanics and Materials, 203 (1), 88–93. doi:10.4028/www.scientific.net/AMM.203.