Improved Assignment Model and Genetic Algorithm for Solving Antiaircraft Weapon-Target Assignment

In the antiaircraft weapon-target assignment, fire resources are easy to be wasted, and damage time could be delayed under the condition of relatively sufficient fire resources. To solve these problems, by combining the damage probability threshold, flying time from target to weapon and the threat degree, the weapon-target assignment model is established under multi constraints. Under the premise of meeting the damage threshold, This model can use as little as possible fire resources to give priority to intercept targets of which flying time is short and threat degree is great. On this basis, the genetic algorithm is used to solve the problem of antiaircraft weapon-target optimal assignment. Aiming at the slow convergence rate of genetic algorithm(GA), the individual and group extremum updating in particle swarm optimization(PSO) is used as the best individual preserving strategy in genetic algorithm, so as to improve the convergence speed of genetic algorithm. Simulation results show the advantages of the new model, as well as the effectiveness and superiority of the improved genetic algorithm.