Site selection

which is one basic task of GIS functionalities

is to search for the best sites for a facility or a number of facilities.The objective is to maximize some utility functions subject to some goals.Traditional site selection methods using GIS only focus on the identifying the best locations（coordinates） of facilities.In many applications

contiguity constraints must be considered in site selection.Site selection should consider not only locations

but also patch configuration for solving many optimization problems.The objective is to maximize utility functions subject to contiguity constraints and various planning goals.The combination of locations and contiguity for site selection is a difficult problem for site selection because of involving huge solution space.The problem becomes more complex when multi-objectives are incorporated in the optimization.Many alternative generating techniques（such as the weighting method and the non-inferior set estimation method） have been developed to help decision-makers search solution spaces.Although these methods are effective under some circumstances

the approaches have several weaknesses:（1） it can only be applied to problems that are mathematically formulated;（2） it is inefficient when applied to large problems;and（3） it may fail to find important solutions.As a consequence

builders of decision-support tools require methods that overcome these limitations and efficaciously generate alternative solutions to multi-objectives decision problems.Particle-swarm optimization（PSO） can be used to achieue such goals.This paper presents a new method to solve such problem by using particle-swarm optimization（PSO） method and shape-mutate algorithms

which is a strict mutation operator to prevent the formation of "holes" in searching for optimal contiguous sites.Particle-swarm optimization method is used to make the solutions flying to the best locations.Shape contiguity constraint and patch configuration optimization are operated by shape-mutate algorithms.Here

a site is represented by using an undirected graph and a set of operations is designed to change the shape and location of sites during the search for possible solutions.These operations evolve randomly generated initial solutions into a set of optimal solutions to this type of problem;at the same time

the contiguity of a site is maintained and the "holes" of the site are prevented to formation.This approach is applied to three different types of cost surfaces: uniform random

a conical and a deformed sombrero-like surface.The analyses are focused on a 128×128 grid of cells

where a facility is located at the center of the area;The number of cells for a site is fixed and set at 10.The results demonstrate the robustness and effectiveness of this PSO-based approach to geographical analysis and multi-objective site selection problems.This approach has also been tested in the city of Guangzhou

to search for the best locations for CBD.The results are also reasonable.The experiments have indicated that this approach is effective in solving this problem.It can successfully capture all the best solutions.The results can be used directly as the location selection by the decision-makers because these have been the best solutions.