ACO is a relatively new meta-heuristic technique and has been successfully used in many applications, particularly in combinatorial optimization problems. The ACO algorithm models the behavior of real ant colonies in establishing the shortest path between food sources and nests. Ants can communicate with each other through chemicals called pheromones in their immediate environment. Ants release pheromones on the ground as they walk from the nest to the food and then back to the nest. Ants move based on the amount of pheromones, the richer the trail of pheromones on a path, the more likely it is to be followed by other ants. So a shorter path has a higher probability of pheromone, the ants will tend to choose a shorter path. Through this mechanism, the ants will eventually find the shortest route. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay Artificial ants mimic the behavior of real ants, but they can solve much more complicated problems than real ants can. ACO has been widely applied to solve various combinatorial optimization problems such as traveling salesman problem (TSP), shop floor scheduling problem (JSP), vehicle routing problem (VRP), quadratic assignment problem ( QAP), etc. Although ACO has a powerful ability to find solutions to combinatorial optimization problems, it has stagnation and premature convergence problems, and the convergence speed of ACO is very slow. Such problems will be more evident as the size of the problem increases. Therefore, several extensions and improved versions of the original ACO algorithm have been introduced over the years. Various adaptations: dynamic control of solution construction, local search fusion, one strategy is to divide artificial ants into two groups: scout ants and common and new ants, pheromone updating strategies are studied, using candidate lists to improve the quality of the final solution and lead to an acceleration of the algorithm. All these studies have contributed to the improvement of ACO to some extent, but have little obvious effect on increasing the convergence speed and obtaining the global optimal solution. Please note: this is just an example. Get a custom paper from our expert writers now. Get a Custom Essay In the proposed system, the major changes introduced by ACO are as follows. First, to avoid research stagnation, ACO is more effective if ants are initially placed in different cities. Secondly, the information entropy that governs the parameters of the algorithm is introduced. Furthermore, the best performing ACO algorithms for the TSP improve the ant-generated solutions using local search algorithms.