Ant may have collected enough information to behavior

Colony Optimization (ACO) is a meta heuristic 
that is mainly used for tackling 
combinatorial optimization (CO) problems. The traveling salesman problem
(TSP) is one of the most important combinational problems.It is known to be NP
hard. ACO is taken as one of the high performance computing  methods for TSP. In ACO algorithm, the
heuristic information is very important in generating high quality
tours.Because the value of the pheromone trails do not get enough information
in the early stage of learning and cannot guide the ants in constructing good
tours, the heuristic parameter may be set to a large value. On the other hand,
in the later stage, the heuristic parameter may need a small value because the
pheromone trails may have collected enough information to behavior as required
and the heuristic information may mislead the search to local optimal solution.
The heuristic parameter is set as a constant in traditional ACO algorithms. It
still has some drawbacks such as stagnation behavior,uneven distribution of
ants, long computational time, and premature convergence problem of the basic
ACO algorithm on TSP. Those problems will be more obvious when the considered
problems size increases. The proposed system based on basic ACO algorithm with
well distribution strategy and information entropy which is conducted on the
configuration strategy for updating the heuristic parameter in ACO to improve
the performance in solving TSP.ACO
is a relatively novel meta-heuristic technique and has been successfully used
in many applications especially problems in combinatorial optimization. ACO
algorithm models the behavior of real ant colonies in establishing the shortest
path between food sources and nests. Ants can communicate with one another
through chemicals called pheromones in their immediate environment. The ants
release pheromone on the ground while walking from their nest to food and then
go back to the nest. The ants move according to the amount of pheromones, the
richer the pheromone trail on a path is, the more likely it would be followed
by other ants. So a shorter path has a higher amount of pheromone in
probability, ants will tend to choose a shorter path. Through this mechanism,
ants will eventually find the shortest path. Artificial ants imitate the
behavior of real ants, but can solve much more complicated problem than real
ants can.

has been widely applied to solving various combinatorial optimization problems
such as Traveling Salesman Problem (TSP), Job-shop Scheduling Problem (JSP),
Vehicle Routing Problem (VRP), Quadratic Assignment Problem (QAP), etc.
Although ACO has a powerful capacity to find out solutions to combinational
optimization problems, it has the problems of stagnation and premature
convergence and the convergence speed of ACO is very slow. Those problems will
be more obvious when the problem size increases. Therefore, several extensions
and improvements versions of the original ACO algorithm were introduced over
the 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 contributed to the improvement of the ACO to
some extents, 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 are the following. First, to avoid search
stagnation and ACO is more effective if ants are initially placed on different
cities. Second, information entropy is introduced which is adjust the
algorithm’s parameters. Additionally, the best performing ACO algorithms for
the TSP improve the solutions generated by the ants using local search
and Artificial Intelligence: An Algorithm is a set of rules that we follow to
achieve some goals.Goals might be some predictions or a solution of any
problem. Articial intelligence is deception of human intelligence process that
emphasizes creation of intelligent machines that works like human
brain.Phenomena of articial intelligence can be achieved by following some
algorithms.Our focus would be to explore the unidentied areas and study its
application on traveling salesman problem(TSP) that means we will predict
solution of traveling salesman problem by following ant colony algorithm(ACO).ACO
is a heuristic based algorithm.This algorithm is used for finding shortest path
by an ant to locate its food.When one ant nds a source of food it returns back
to it’s colony leaving behind some marks called pheromones. Next ant when come
across marks it likely to follow same path to an extent.If second ant does the
same it leaves its own marks and it gets stronger until bunch of ants traveling
to various food sources near the colony.