Dynamic ant colony optimisation for TSP

Ants exhibit collective behaviour 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 behaviour and pheromones, Marco Dorigo proposed the ant algorithm and applied it to solving the travelling salesman problem (TSP) and solving job-shop scheduling. In this paper, we simulate real ants with more aspects. Updating of pheromones is more likely to be the real situation in the natural world. Our algorithm shows a better performance than the original algorithm.