A two-step evolutionary and ACO approach for solving the multi-agent patrolling problem

Patrolling an environment involves a team of agents whose goal usually consists in continuously visiting its most relevant areas as frequently as possible. For such a task, agents have to coordinate their actions in order to achieve optimal performance. Current research that tackles this complex multi-agent problem usually defines the environment as a graph, so that a wide range of applications can be dealt with, from computer network management to computer games and vehicle routing. In this paper, we consider only the instances of the multi-agent patrolling problem where all the agents are located on the same starting node. These instances are often encountered in robotics applications, where e.g. drones start from the same area, disperse over it and finally patrol around distant locations. We introduce a new ant colony optimization (ACO) algorithm that is combined with an evolutionary algorithm (EA) technique. The novel ACO algorithm uses several ant colonies that are engaged in a competition for finding out the best multi-agent patrolling strategy. The goal of the EA is to find the best set of distant nodes enabling each agent to disperse efficiently over the graph. Experimental results show that, irrespective of the number of the involved patrolling agents and for all the graphs evaluated, our two-step EA and ACO algorithm outperforms significantly and with efficiency the best techniques proposed in the literature since now.