A receding horizon genetic algorithm for dynamic multi-target assignment and tracking: A case study on the optimal positioning of tug vessels along the northern Norwegian coast

Combining methodologies from cybernetics and artificial intelligence (AI), we present a receding horizon genetic algorithm (RHGA) for solving the task of dynamic assignment and tracking of multiple targets. We demonstrate the capabilities of the algorithm by means of a case study on optimal positioning of tugs to reduce the risk of oil tanker drifting accidents along the northern Norwegian coast. Through simulations we show that the RHGA performs intelligent target assignment and close target tracking while constantly reevaluating its suggested solutions based on current and predicted information. We see great potential for further development and consider our RHGA and problem description a platform for further research.