CO it’s algorithm models the behavior of

CO which is  meta-heuristic technique and has been successfully used in many applications. it’s  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 pheromone.  Ant releases a fluid called pheromones when the move around. Intensity of pheromones dropped by an ant defines way for the number of ant following the pheromones. Higher the number of pheromones more chances to follow that ant lesser the number of pheromones less probability of that ant to be followed by any other ant. Shorter path has high amount of pheromone in probability and larger path have lower amount of pheromones on probability. By this way ants can ultimately follow th shorter path and artificial ants can solve complicated problems more than the real ants.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. Beside the fact that ACO is one of the most suitable solutions to solve Combinational optimization  and  huristic parameter that is set as a constant in traditional Ant Colony Algorithm has some drawbacks that includes stagnation behavior, uneven distribution of ants, long computational time and premature convergence problem .As the problem size increases problems become more obvious  .Therefore, several extensions and improvements versions of the original ACO algorithm were introduced over the years. Various adaptations: dynamic control of solution constructioncite{ghafurian2011ant} , mergence of local searchcite{oliveira2017analysis} , a strategy is to partition artificial ants into two groups: scout ants and common ants  and new pheromone updating strategies, cite{swikatnicki2015application}  using candidate lists cite{jun2012application},cite{oliveira2017analysis},cite{shi2015research} strategies  are studied to improve the quality of the final solution and lead to speedup of the algorithm.cite{mahi2015new},cite{othman2008dacs3},cite{othman2009strategies} 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.To avoid drawback and to minimize it’s affect there are several modifications made in the ACO and those are listed first To avoid search stagnation ants should be places at different cities. Second  information entropy is introduced which adjusts the algorithm’s parameters.  Best performing ACO algorithms can improve the solution generated by ants using local search algorithm in TSP.