Routing Vehicles Using Genetic Algorithm and Ant Colony

The ant colony algorithm is inspired by observations and observations on the ants colony. These studies have shown that ants are social insects that live in colonies and that their behavior is more toward cloning than to survive a component. One of the most important and most interesting behavior of ants is their behavior in finding food, and in particular how to find the shortest path between food and nourishment. An ant colony algorithm uses two features of the pheromone evaporation and accident probability to optimize problems such as the vendor.Working on the development of intelligent systems inspired by nature is a very popular field of artificial intelligence. Genetic algorithms that are based on the Darwinian evolutionary idea and natural selection are a great way to find optimization issues. The Darwinian evolutionary idea suggests that each generation has evolution over the previous generation, and what happens in nature is the result of millions of years of evolution of the generation to the generations of creatures like the ant.A timetable window is a table in which different timing routes are encoded for the vehicle so that the user or system automatically predicts the best path for the vehicle. Since network topology is not constant, routing in dynamic networks is a challenging activity. This issue is discussed in this study using the ant colony algorithm to consider the networks that use such packets. The paths generated by the ant s algorithm are the input data for the genetic algorithm. The genetic algorithm finds a set of suitable paths. The importance of using an ant colony algorithm is to reduce the size of the path table. The genetic algorithm is based on the principle of route development rather than saving pre-calculated paths.The genetic algorithm is always trying to find more effective solutions to dynamic problems using generational synthesis concepts and genetic mutations. Anthony colony Ants live more in community and live in colonies of 30 million, with ants having more than 4500 species.

Keywords:

genetic algorithm , ant colony , routing , vehicle navigation