Research on Stamping Modeling Based on Multiobjective Fusion and Multistrategy Improved Grey Wolf Optimization Algorithm

With the rapid development of modern manufacturing, stamping technology plays a crucial role in various industries due to its efficiency and precision. However, the complex nonlinear characteristics of stamping modeling and optimization pose significant challenges. Traditional single-objective optimization methods often fail to achieve a balance between multiple conflicting objectives such as quality, efficiency, and cost. To address these issues, this study proposes a stamping modeling optimization method based on multiobjective fusion and the multistrategy improved grey wolf optimization algorithm (MOF-MSIGWO). The proposed algorithm balances global exploration and local development by introducing an adaptive weight mechanism, combines mixed strategies such as chaotic mapping and differential evolution to enhance global search capability, and applies Pareto frontier mechanism to handle multiobjective tradeoff problems. The experiment was conducted in typical stamping modeling, with optimization objectives including minimizing rebound error, reducing material usage, and improving production efficiency. The experimental results show that the proposed algorithm exhibits better performance compared to traditional optimization methods. For example, rebound error decreased by 12.8%, material usage decreased by 10.5%, and production efficiency increased by 15.3%. In addition, compared with the standard grey wolf optimization algorithm, the MOF-MSIGWO algorithm improved the super volume index by 25%, indicating that the algorithm has better convergence and diversity. This study demonstrates the effectiveness of integrating multiple strategy improvements into metaheuristic algorithms in complex manufacturing optimization, providing a practical solution for improving the efficiency and quality of stamping processes. The successful application of the GWO algorithm in the field of image segmentation has also brought significant improvements in computational efficiency. Compared to traditional segmentation algorithms, the GWO algorithm can complete high-quality segmentation tasks in a shorter time, thus meeting the needs of real-time image processing and large-scale image data set processing.