Akademik Veri Yönetim Sistemi
7th International Conference on Scientific and Innovative Studies (ICSIS 2026), Konya, Türkiye, 16 - 17 Nisan 2026, cilt.1, sa.1, ss.268-273, (Tam Metin Bildiri)
The increasing complexity of modern avionics systems presents numerous challenges, including reliability, energy efficiency, response time, and safety. Simultaneous optimization of often conflicting performance criteria has become essential. This study proposes a hybrid artificial intelligence (AI)-based optimization method for the design and performance improvement of avionics systems. The proposed method first efficiently scans the solution space. Subsequently, it combines optimization algorithms with machine learning-based evaluation mechanisms to determine the optimal configurations under different operational constraints. Furthermore, it operates with a multi-objective cost function to balance system stability, computational load, fault tolerance, and real-time processing requirements, considering core avionics subsystems such as flight control, navigation, and communication. Additionally, convergence speed and solution quality are enhanced through a hybrid optimization strategy that combines meta-heuristic algorithms with local optimization mechanisms. Adaptive weighting techniques, which prioritize targets based on mission profile and environmental conditions, are also integrated into the model. Simulation studies show that the proposed method exhibits superior performance compared to traditional approaches in terms of solution diversity, convergence efficiency, and resilience to uncertainties. The results also demonstrate that the usability of modern avionics systems can be increased. In conclusion, this study offers a scalable and flexible solution for next-generation intelligent avionics systems, contributing to the improvement of their autonomy and decision-making capabilities.