Efficient Radar Scheduling Using Genetic Algorithms and Stochastic Heuristic Initialization

Optimal radar scheduling is essential for ensuring the safety and security of critical areas. It requires a balance between maximizing coverage and managing operational constraints. This study addresses the complex Radar System Scheduling Problem, which involves efficiently allocating various radars across multiple sessions to maximize coverage while minimize the number of radars across all sessions. We propose a specialized Genetic Algorithm with custom operators to handle these constraints effectively. Additionally, we introduce a Stochastic Heuristic Initialization method to dynamically prioritize radar assignments, enhancing the flexibility and robustness of the scheduling process. By combining heuristic-based initialization, a constraint-preserving recombination operator, and a customized mutation method, our approach generates high-quality solutions that respect the problem's many constraints. Our research demonstrates the adaptability of genetic algorithms to real-world problems, showing significant improvements in scheduling efficiency and operational flexibility.