Simulation on ant colony optimization for TSP

Ants exhibit collective behavior in performing tasks that cannot be carried out by an individual ant. When ants are working, they must communicate with each other through a kind of chemical substance-pheromones. Ants look for food and lay the way back to their nest with pheromones, and the other ants can follow the pheromone to find the food efficiently. Using the analogy of foraging behavior and pheromones, Marco Dorigo proposed the ant algorithm and applied it to solving the traveling salesman problem (TSP) and solving job-shop scheduling [1-3]. In this paper, we simulate real ants with more aspects to introduce the principles, algorithm implementation and simulated experiment on TSP problem.