Abstract:
At the point when the size of the shortest path discovering problem increment, the intricacy of the issue
increments. In such a situation, the best possibility to take care of this sort of issue is meta-heuristic
methodology. Conversely, notwithstanding the way that heuristic strategies can't locate an optimum
solution, they can discover a sub-ideal solution inside an acceptable time limit. Moreover, since
conventional algorithms don't have a decent structure for getting away from local optima, they can't
converge to a great solution. In this way, the heuristic calculations which utilize random structures for
finding solutions have been proposed. Such calculations called meta-heuristic can escape from local
optimum points however much as could be expected and unite to good solutions. In this study, we have
proposed the procedure to identify and eliminate the cycle in the decision-making process of the ant colony
system. The aim of this study is to show the number of ants stuck in the cycle of the graph if eliminated
results in improvements of the computational efficiency.
