ACO behavior in colonies in establishing the shortest path

ACOis a relatively novel meta-heuristic based technique which has beensuccessfully applied for combinatorial optimization problems. ACO algorithmmodel is based upon real ant behavior in colonies in establishing the shortestpath between food sources and nests. Ants communicate with one another throughpheromones – a chemical. The ants discharge pheromone on their way whilewalking from their nest to food and then follow it back to the nest. The antsmove conferring to the amount of pheromones discharges, it is more likely thatthe ants will be following the denser the pheromone trail. So a shorter pathhas a higher amount of pheromone in probability, hence resultantly ants willtend to pick out a shorter path. Through this apparatus, ants will ultimately endup with a shortest path.

Artificial ants imitate the behavior of real ants, butare capable of solving more complex problems.Variouscombinatorial optimization problems such as Traveling Salesman Problem (TSP),Job-shop Scheduling Problem (JSP), Vehicle Routing Problem (VRP), QuadraticAssignment Problem (QAP), etc has been effectively solved through ACO. Althoughit has an influential capacity to discover solutions to combinationaloptimization problems, it faces stagnation and premature convergence; moreover,the convergence speed of ACO is very slow. These problems are to be more noticeablewhen the problem size increases. Therefore, several allowances and enhancedversions of the original ACO algorithm were presented over the past years.Various adaptations: dynamic control of solution construction 4, mergence oflocal search 3, 13, a strategy is to partition artificial ants into twogroups: scout ants and common ants 11 and new pheromone updating strategies1, 3, 14, using candidate lists strategies 2, 16, 17 are studied to improvethe quality of the final solution and lead to speedup of the algorithm.

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Allthese studies have resulted in the upgrading of the ACO to some extent, butthey have little obvious effect on increasing the convergence speed andobtaining the global optimal solution. In the proposed system, the main modificationsintroduced by ACO is firstly, to evade search stagnation and ACO is more functionalif ants are initially placed on different cities. Second, algorithm’sparameters are adjusted in information entropy. Additionally, the bestperforming ACO algorithms for the TSP improve the solutions generated by theants using local search algorithms.