Optimization Based on Simulation of Ants Colony

Abstract

Natural processes optimize life on earth for thousands of years, so people are inspired by many problem-solving techniques in nature. Metaheuristics inspired by natural processes and systems have become a very active field of research in recent years. One of the most popular methods is Ant Colony Optimization (ACO). In this paper is considered the application of Ant Colony Optimization in the case of the Traveling Salesman Problem (TSP). Different cases, with a different number of ants (population size) with a different number of iteration using software simulation, are considered. It is shown that Roulette Wheel Selection has some impact on the speed of the result. On the other hand, with more ants in each iteration, we get more constructed solutions, which increases the probability of finding a better solution.