Prevention of Very Quick Convergence of the Algorithm

Quick convergence shows the situation, there the solution obtains entrapped in local optima and there is no probability of generating a newer path. Such effect can be prevented by employing the quantity qc₀, explain as:

                                 qc₀ = log_e(n)/log_e(N_max)            0< qc₀<1................................Eq(5)

From Eq5, this can easily be visualized that along with the raise in number of iterations, quantity qc₀ initialize its value from 0 and slowly it attains the value like 1, while quantity n becomes equal to N_max. The quantity qc₀ is compared along with a randomly made quantity 'δ'. Moreover, equation 5 represents that for less numbers of iterations, the possibility of occurrence of event qc₀ < δ will be higher; thus, ants will randomly select the node. While, as the number of iterations raised, the probabilistic transition rule will be applied to select the next node. Thus, in the initial stages of the search, algorithm knowing new path in a higher pace as compared to later stages in order to ignore the local entrapment of the solution.