Improved ant colony optimization based on particle swarm optimization and its application

This article introduces a novel algorithm to solve the large time-consuming problem of the existing improved ant colony optimization (ACO) based on particle swarm optimization (PSO). A new pheromone update method which combines the global asynchronous feature and elitist strategy was used in the algorithm. Moreover, the iteration steps of ACO invoked by PSO were reasonably reduced. The algorithm was applied to solve the path planning problem of landfill inspection robots in Asahikawa, Japan. It is shown that the algorithm has a better performance in search speed compared with other algorithms recently reported.