ACO behavior in colonies in establishing the shortest path

is a relatively novel meta-heuristic based technique which has been
successfully applied for combinatorial optimization problems. ACO algorithm
model is based upon real ant behavior in colonies in establishing the shortest
path between food sources and nests. Ants communicate with one another through
pheromones – a chemical. The ants discharge pheromone on their way while
walking from their nest to food and then follow it back to the nest. The ants
move conferring to the amount of pheromones discharges, it is more likely that
the ants will be following the denser the pheromone trail. So a shorter path
has a higher amount of pheromone in probability, hence resultantly ants will
tend to pick out a shorter path. Through this apparatus, ants will ultimately end
up with a shortest path. Artificial ants imitate the behavior of real ants, but
are capable of solving more complex problems.

combinatorial optimization problems such as Traveling Salesman Problem (TSP),
Job-shop Scheduling Problem (JSP), Vehicle Routing Problem (VRP), Quadratic
Assignment Problem (QAP), etc has been effectively solved through ACO. Although
it has an influential capacity to discover solutions to combinational
optimization problems, it faces stagnation and premature convergence; moreover,
the convergence speed of ACO is very slow. These problems are to be more noticeable
when the problem size increases. Therefore, several allowances and enhanced
versions of the original ACO algorithm were presented over the past years.
Various adaptations: dynamic control of solution construction 4, mergence of
local search 3, 13, a strategy is to partition artificial ants into two
groups: scout ants and common ants 11 and new pheromone updating strategies
1, 3, 14, using candidate lists strategies 2, 16, 17 are studied to improve
the quality of the final solution and lead to speedup of the algorithm. All
these studies have resulted in the upgrading of the ACO to some extent, but
they have little obvious effect on increasing the convergence speed and
obtaining the global optimal solution. In the proposed system, the main modifications
introduced by ACO is firstly, to evade search stagnation and ACO is more functional
if ants are initially placed on different cities. Second, algorithm’s
parameters are adjusted in information entropy. Additionally, the best
performing ACO algorithms for the TSP improve the solutions generated by the
ants using local search algorithms.

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