Air Defense Deployment of Anti-reconnaissance Based on Immune Optimization Algorithm with Nested Double Particle Swarm

The position deployment and firepower allocation of weapons are very important in military conflict. To the anti-reconnaissance aircraft cluster problem of multi-type air defense weapons, considering the location and fire distribution of different types of air defense weapons simultaneously, a multi-objective mixed deployment model is established taking the overall effectiveness and cost of air defense as the optimization objectives. An immune optimization algorithm with nested double particle swarm is designed to solve the model. Through a series of comparative analysis of algorithm parameters and performance, the proposed algorithm is verified, and the final simulation results illustrate the feasibility and effectiveness of the model and algorithm.