A Gravitational Search Algorithm-Based SingleCenter of Mass Flocking Control for Tracking Single and Multiple Dynamic Targets for Parabolic Trajectories in Mobile Sensor Networks

Development of an optimal flocking control procedure is an essential problem in mobile sensor networks (MSNs). Furthermore, finding the parameters such that the sensors can reach the target in an appropriate time period is an important issue. This paper offers an optimization approach based upon the metaheuristic methods used for flocking control in MSNs to follow a target. We develop a non-differentiable optimization technique based on the gravitational search algorithm (GSA). Finding the flocking parameters using swarm behaviors is the main contribution of this paper in order to minimize the cost function. The cost function displays the average Euclidean distance of the center of mass (COM) away from the moving target. One of the benefits of using GSA is its application in multiple targets tracking with satisfactory results. The simulation results obtained that this scheme outperforms the existing ones, and demonstrate the ability of this approach in comparison with the previous methods.