ACO is a relatively new meta-heuristic technique and has been used successfully in many applications, particularly in combinatorial optimization problems. The ACO algorithm models the behavior of real ant colonies by establishing the shortest path between food sources and nests. Ants can communicate with each other using chemicals called pheromones in their immediate environment. Ants release pheromones onto the ground as they walk from their nest to food and then return to the nest. Ants move based on the amount of pheromones, the richer the pheromone trail on a path, the more likely it is to be followed by other ants. So a shorter path has a higher probability of pheromone, ants will tend to choose a shorter path. Thanks to this mechanism, the ants will eventually find the shortest path. Say no to plagiarism. Get a tailor-made essay on "Why violent video games should not be banned"?Get the original essayArtificial ants imitate the behavior of real ants, but can solve much more complicated problems than real ants. ACO has been widely applied to solving various combinatorial optimization problems such as the traveling salesman problem (TSP), the shop floor scheduling problem (JSP), the vehicle routing problem (VRP), the quadratic allocation (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. These problems will be more evident as they increase in size. Therefore, several versions of extensions and improvements to the original ACO algorithm have been introduced over the years. Various adaptations: dynamic control of solution construction, fusion of local search, one strategy consists of dividing artificial ants into two groups: scout ants and common ants and new Strategies for updating pheromones, using candidate list strategies, are studied 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 they have little obvious effect on increasing the convergence speed and obtaining the overall optimal solution. Keep in mind: this is just a sample. Get a custom paper now from our expert writers.Get a Custom EssayIn the proposed system, the main changes introduced by the ACO are as follows. First, to avoid research stagnation, ACO is more effective if ants are initially placed in different cities. Second, information entropy is introduced to adjust the algorithm parameters. Additionally, the best-performing ACO algorithms for TSP improve ant-generated solutions using local search algorithms..