Application of ant colony optimization method in GIS

Swarm intelligence is one of the new growing methods that is considered in artificial intelligence as a function of the social interaction of components. The Basics of swarm intelligence are based on the study of the behavior of social organisms such as some insects (bees, ants, termites) or even humans. The issue of using meta-heuristic methods for application in hybrid optimization problems is a rapidly growing field of research. This is due to the importance of hybrid optimization issues in the world of industry and science. In recent years, one of the most important and promising researches has been "supra-innovative methods derived from nature", which has had very good results in solving problems of combined problems. Meta-heuristic algorithms are used to solve a problem when, as the size of the problem increases dramatically, so-called NP-hard problems. One of the most widely used meta-innovative methods in this field is the ant colony optimization algorithm, which is used today in solving the problems of spatial resource allocation, routing, and location in GIS environments. In this research, while examining the ant colony algorithm, its expression and parameters required for use in the GIS environment are discussed. The ability of algorithms based on food search in the ant colony algorithm is significantly dependent on the optimal determination of the parameters in these algorithms.