Machine Learning Techniques for Optimization in Engineering Applications - Damodar S. Hotkar (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Engineering depends greatly on optimization, as efficiency, accuracy and lowering costs are what really matter. Lately, people have turned to machine learning as an effective way to face these problems. This article discusses using different ML techniques like supervised, unsupervised and reinforcement learning for optimization in the fields of structure, energy, manufacturing and transport. The research investigates the benefits and drawbacks of the approaches, reviews ongoing studies and provides a comparison analysis referring to benchmark data and simulations. The research reveals that using ML for optimization can lead to faster results, greater adaptability and higher accuracy than using traditional approaches. In the end, the paper outlines new trends and recommends topics for further research.