Weapon–Target Assignment Using a Whale Optimization Algorithm

Abstract

The weapon-target assignment (WTA) is a well-known task distribution issue in complicated combinational optimization, which is essential to management and decision-making in marine coordinated combat. This paper proposes a whale optimization algorithm (WOA) to address the WTA and the purpose was to maximize the combat effectiveness and determine the greatest decision matrix while equitably distributing the weapon unit resources of the weapon systems to the threat targets. The WOA is based on the whales’ bubble-net assaulting behavior that mimics encompassing contraction strategy, bubble-net assaulting strategy and random hunting strategy to successfully resolve the optimization issue. The WOA not only has excellent stability and robustness to determine a quicker convergence speed and greater calculation accuracy but also utilizes exploration or exploitation to avoid search stagnation and accomplish the most effective solution. Four sets of experiments are utilized to confirm the superiority and productivity of the WOA, the results are compared with those of AOA, BA, GWO, MVO, SCA, SOA, SSA and TSA by maximizing the fitness value. The experimental results demonstrate that WOA has a greater convergence precision and stronger optimization efficiency, which is a practical and feasible method to satisfy the fundamental requirements of real-time decision-making.