A new grid algorithm for GIS visualization

The task of GIS visualization has a large amount of data and a great deal of information factors, the performance of ant traditional algorithm would rapidly deteriorate if it is applied to a large-scale problem to process task decomposition. The main reason is that ant algorithm is just like ants looking for food which mostly depend on information hormone and in the initial phase of the algorithm, external levels of information hormone are basically equal to each other which results in ants' blindfold search, and only after a long time the external levels of information hormone would present obvious instructive function. Furthermore ant algorithm is a kind one of positive feedback, so it relapses easily into local optimization while rapid convergence. Therefore in the paper we improved the basic ant algorithm and designed the modulation strategy of GIS visualization resources based on the improved ant colony optimization algorithm. And the improved ant colony optimization algorithm in our paper gave attention to combination of grid task-modulation and local task-modulation and offered client ports the visualized tools of task-submission and exerting circumstance inspection. Experimental results show by improving ant algorithm, our system not only decreases many disadvantages of the traditional ant algorithm, but also like ants looking for food effectively distributes the complicated GIS data to many computers to process cooperatively and gains a satisfying search result.