Intelligent GIS for solving high‐dimensional site selection problems using ant colony optimization techniques

This paper presents a new method to solve site selection problems using ant colony optimization (ACO) techniques. Optimal spatial search for siting public facilities is a common task for urban planning. The objective is to find N optimal sites (targets) for sitting a facility so that the total benefits are maximized or the total costs are minimized. It is straightforward to use the brute‐force method for identifying the optimal solution by enumerating all possible combinations. However, the brute‐force method has difficulty in solving complex spatial search problems because of a huge solution space. Ant colony optimization can provide a useful tool for site selection. In this study, the integration of ACO with geographic information systems is proposed to include various types of spatial variables in the optimization. A number of modifications have also been introduced so that ACO can fit spatial allocation problems. The novelty of this research includes the adoption of the strategies of neighborhood pheromone diffusion, tabu table adjusting, and multi‐scale optimization. This method has been applied to the allocation of a hypothetical facility in Guangzhou City, China. The experiment indicates that the proposed model has better performance than the single search and the genetic algorithm for solving common site search problems.

[1]  P. Steadman,et al.  On the geography of a worst case nuclear attack on the population of Britain , 1982 .

[2]  Charles S. ReVelle,et al.  The Location of Emergency Service Facilities , 1971, Oper. Res..

[3]  Anthony Gar-On Yeh,et al.  Integration of genetic algorithms and GIS for optimal location search , 2005, Int. J. Geogr. Inf. Sci..

[4]  Marco Dorigo,et al.  Distributed Optimization by Ant Colonies , 1992 .

[5]  Richard L. Church,et al.  Geographical information systems and location science , 2002, Comput. Oper. Res..

[6]  Wang Yin Chai,et al.  An Intelligent GIS-Based Spatial Zoning System with Multiobjective Hybrid Metaheuristic Method , 2004, International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems.

[7]  M. Dorigo,et al.  1 Positive Feedback as a Search Strategy , 1991 .

[8]  Marco Dorigo,et al.  Ant algorithms and stigmergy , 2000, Future Gener. Comput. Syst..

[9]  P. Hansen,et al.  Variable neighborhood search for the p-median , 1997 .

[10]  Ralph W. Kiefer,et al.  COMPUTER-BASED LAND USE SUITABILITY MAPS , 1973 .

[11]  Wang Yin Chai,et al.  An intelligent GIS-based spatial zoning system with multiobjective hybrid metaheuristic method , 2004 .

[12]  Stan Openshaw,et al.  Artificial intelligence in geography , 1997 .

[13]  A. Yeh,et al.  An integrated GIS and location-allocation approach to public facilities planning : An example of open space planning , 1996 .

[14]  G. Rushton.  Use of Location-Allocation Models for Improving the Geographical Accessibility of Rural Services in Developing Countries , 1984 .

[15]  Jerome E. Dobson.  A Regional Screening Procedure for Land Use Suitability Analysis , 1979 .

[16]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[17]  Alan Wilson,et al.  Intelligent GIS: Location Decisions and Strategic Planning , 1996 .

[18]  S. L. HAKIMIt.  AN ALGORITHMIC APPROACH TO NETWORK LOCATION PROBLEMS. , 1979 .

[19]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[20]  Ping Shi,et al.  Retrieval of water optical properties for optically deep waters using genetic algorithms , 2003, IEEE Trans. Geosci. Remote. Sens..