An improved ant colony optimization algorithm based on dynamically adjusting ant number

Abstract

The ant colony algorithm is a mature and effective method to solve the problem of optimizing shortest path, which is one of the key technologies for robot navigation and path planning. But the algorithm often fails into precocity easily and can't get the global best result. This paper proposes an improved ant colony optimization algorithm by dynamically adjusting ant number. The main idea of this algorithm is that only the part of the ants passing the shorter path is allowed to release pheromone and update the total ant number randomly or fixedly in algorithm iterative process. So, the improved algorithm can increase the randomness in the search and improve global search ability. To verify the performance of this algorithm, this paper uses the improved algorithm to solve Chinese Traveling Salesmen Problem. The simulation results show that compared with the traditional ant colony algorithm, the improved ant colony algorithm is easier to find the optimal solution, and its optimization ability is stronger.