Ant

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.

ACO

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

algorithms.Algorithms

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.