Modern battlefield environments are filled with complexity and uncertainty, necessitating accu- rate assessment of threats posed by targets, armaments, and protective assets. In these complex settings, objective threat assessment is crucial for strategic decision-making and efficient resource allocation. This study proposes a new methodology for threat assessment, integrating fuzzy logic with genetic algorithms. Utilizing real-world data on weaponry and equipment, this methodology derives optimal membership function values using genetic algorithms. These val- ues are then used to extract threat weights for each piece of equipment. Additionally, proximity calculations are employed to determine the final threat level. This approach offers a more objec- tive and precise evaluation compared to traditional methods, effectively reflecting the diverse characteristics of ground targets. Future research will focus on developing algorithms that con- sider a broader range of battlefield conditions and target characteristics, along with validating their applicability in real-world scenarios.
