OPTIMIZATION OF COMPUTER GAME PERFORMANCE USING MACHINE LEARNING METHODS - Zavhorodnia, G. A.; Zavhorodnii, V. V. (Systems and Technologies, 2026)
The article presents a comprehensive method for optimizing the performance of computer games based on machine learning techniques. The proposed approach combines GPU load prediction using deep neural networks (DNN) with adaptive real-time management of rendering parameters, enabling the automatic determination of optimal graphics settings to maintain stable frame rates (FPS), reduce GPU load fluctuations, and preserve high visual quality. A mathematical efficiency model is for- mulated, integrating frame rate, graphics quality, and GPU utilization into an optimization function with weighted coefficients reflecting user priorities. The approach includes adaptive adjustment of graphics parameters based on gradient descent and predictive modeling of computational load, allowing dynamic resource management without developer intervention. To evaluate the effectiveness of the proposed method, comparative experiments were conducted against traditional optimization strategies, including static configuration and linear GPU load prediction. The results demonstrate a 20–30 % increase in FPS, a 10–12 % reduction in average GPU load while maintaining high graphics quality (≈ 98 % of maximum), and a significant reduction in frame rate fluctuations in complex scenes. The prototype implementation, developed in Python, was successfully integrated into modern game engines such as Unity and Unreal Engine, confirming the practical applicability of the method in real-world projects. The scientific novelty of this research lies in the integration of multiple classes of machine learning models into a sin- gle adaptive rendering optimization system, capable of considering both hardware parameters and user behavior in real time. The proposed framework provides a foundation for further development of adaptive ML-based systems in game development, including VR/AR and cloud gaming platforms, as well as for enhancing the efficiency of game production pipelines with high performance and graphics quality requirements. Overall, this method demonstrates the potential to substantially improve real-time performance optimization in interactive entertainment applications while maintaining visual fidelity, providing valuable insights for both academic research and industry practice.