An ant colony algorithm with the 2nd Newton Law

This paper presents an ant colony optimization algorithm with the 2nd Newton Law. We couple a group of parameters with the basic ant colony approach to handle the balance between the convergent speed and the global solution searching ability. This approach narrates the pheromone increasing style with the 2nd Newton law, and some new parameters named agglomeration and acceleration are used to describe the basic parameters just as α, β, ρ, Q and M for controlling the selection probability of ants. This paper use the travel time to decide the best resolution. At last, the viability of the approach has been tested with some travel salesman problems and encouraging results have been obtained.