Optimizing UAV decoy deployment against infrared and optical-guided missiles using an adaptive genetic algorithm approach

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in mission-critical operations, making their survivability against infrared and optical-guided missile threats paramount. This paper presents an optimized decoy deployment strategy for UAVs using an Adaptive Genetic Algorithm (AGA). The primary objectives are to maximize demand satisfaction at decoy locations while minimizing resource overuse and time window violations. Through simulations across three distinct scenarios, we demonstrate the AGA’s effectiveness in optimizing these objectives under varying constraints. The results indicate a significant reduction in penalties related to unmet demand and resource overuse, showcasing the AGA’s capability to adapt dynamically to different operational challenges. Comparative analysis highlights the AGA’s superior convergence efficiency and adaptability relative to traditional optimization methods. These findings underscore the potential of AGA-based approaches in enhancing UAV survivability against sophisticated missile threats.