Air defense target allocation based on improved genetic algorithm - Zhao, Xia; Shen, Hai; Li, Dazhe (SPIE, 2023)
With the development of information technology, the battlefield forms in air defense operations are complex and changeable, the target allocation is subject to many restrictions, and the real-time requirements of operations are higher, which makes the results of traditional air defense operations unsatisfactory. Based on this, an improved genetic algorithm is proposed for air defense target assignment. This algorithm first sets relevant parameters and establishes a fitness function, then randomly generates an initial population, and then uses genetic operators to optimize the population from generation to generation, and finally judges whether the maximum genetic generation is reached, and if so, ends Algorithm, continue to optimize. The experimental results show that the average total benefit value of the improved genetic algorithm is 6.42525, which is significantly better than the traditional genetic algorithm. In addition, compared with the traditional genetic algorithm, the improved genetic algorithm has faster convergence speed and high search efficiency. More importantly, the improved genetic algorithm is more reasonable in target allocation and has better practical value in air defense operations.