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.
gis site selection ant colony optimization artificial intelligence