Weapon Target Assignment for Surface-to-air Defense with an Improved Reinforcement Genetic Algorithm

Decision optimization in surface-to-air defense and coordinated weapon target assignment can improve the defense effect and the utilization rate of weapons. Considering the constraints of weapon capability, a multi-target optimization model is established to maximize the damage probability and minimize the weapon consumption cost. A dual population genetic algorithm improved by reinforcement learning is designed to solve this model. The chromosome exchange ratio is controlled by reinforcement learning to ensure population diversity. Experimental results verify the convergence speed and accuracy of the improved reinforcement genetic algorithm.