An Improved Ant Colony Optimization Algorithm for Multi-Agent Path Planning

In recent years, Ant Colony Optimization algorithm has become one of the most widely used heuristic algorithms and has been apply to solve different types of path planning problems. However, there still are some problems in Multi-Agent Path Finding, such as low convergence efficiency, easy to fall into local optimum and vertex conflict. In this paper, we proposed an Improved Ant Colony Optimization algorithm based on parameter optimization and vertex conflict resolution. First of all, we initialize the distribution of pheromones to reduce the blindness of the algorithm in the early stage. Secondly, we introduce an adaptive pheromone intensity and pheromone reduction factor to avoid the algorithm falling into local optimum. On this basis, the algorithmÿs global search ability and convergence speed are improved by dynamic modification of the evaporation factor and heuristic function. In addition, the strategy of dynamically modifying the influence factor and heuristic function improves the global search ability and convergence speed of the algorithm. To solve vertex conflict in MAPF, we use the design conflict prediction and resolution strategy to effectively avoid vertex conflict and improve the reliability of the multi-agent system. Simulation experiments verify the effectiveness and adaptability of IACO under different complexity environments, and prove that IACO has good convergence speed and path global optimization ability.