optimization of steel alloy composition to maximize yield strength using a machine learning model and the cuckoo optimization algorithm

Designing high-yield-strength steel alloys remains a key challenge in materials engineering, as traditional trial-and-error approaches are costly and inefficient. this study presents an intelligent two-stage framework integrating machine learning and metaheuristic optimization to accelerate alloy discovery. first, a random forest model was trained on experimental data, achieving a high predictive accuracy for yield strength (r² = 0.8194, mse = 12445.02). this model was then employed as the objective function within the cuckoo optimization algorithm (coa). after 100 iterations, coa identified an optimal alloy composition with a yield strength of 2456.46 mpa, significantly exceeding the maximum value in the original dataset. the optimized composition features substantial percentages of key strengthening elements: cobalt (11.19%), chromium (10.61%), molybdenum (6.04%), and tungsten (4.26%), aligning with known solid-solution and carbide precipitation mechanisms. these results confirm that combining machine learning with metaheuristic optimization provides a powerful, efficient pathway for designing novel alloys, promising to drastically shorten the materials development cycle.