Decentralized Algorithms for Weapon-Target Assignment in Swarming Combat System

Abstract

Swarming small unmanned aerial or ground vehicles (UAVs or UGVs) have attracted the attention of worldwide military powers as weapons, and the weapon-target assignment (WTA) problem is extremely significant for swarming combat. The problem involves assigning weapons to targets in a decentralized manner such that the total damage effect of targets is maximized while considering the nonlinear cumulative damage effect. Two improved optimization algorithms are presented in the study. One is the redesigned auction-based algorithm in which the bidding rules are properly modified such that the auction-based algorithm is applied for the first time to solve a nonlinear WTA problem. The other one is the improved task swap algorithm that eliminates the restriction in which the weights of the edges on graph G must be positive. Computational results for up to 120 weapons and 110 targets indicate that the redesigned auction-based algorithm yields an average improvement of 37% over the conventional auction-based algorithm in terms of solution quality while the additional running time is negligible. The improved task swap algorithm and the other two popular task swap algorithms almost achieve the same optimal value, while the average time-savings of the proposed algorithm correspond to 53% and 74% when compared to the other two popular task swap algorithms. Furthermore, the hybrid algorithm that combines the above two improved algorithms is examined. Simulations indicate that the hybrid algorithm exhibits superiority in terms of solution quality and time consumption over separately implementing the aforementioned two improved algorithms.