Enhancement of Multidisciplinary Collaborative Optimization Using Response Surface Methodology

In this research, we focus on refining multidisciplinary collaborative optimization methods to achieve superior performance and efficiency. By implementing response surface methodology as a surrogate modeling technique within the existing collaborative optimization framework, we developed an enhanced algorithm that approximates complex system responses through polynomial regression models. The improvement involves constructing quadratic response surfaces using central composite design experiments to reduce computational expense while maintaining optimization accuracy. Through detailed numerical case studies with code implementations in MATLAB/Python, we validated the effectiveness of the modified collaborative optimization method. Our study demonstrates that the algorithm enhancement significantly improves system performance metrics by 15-30% compared to baseline methods, providing more robust solutions for practical engineering applications through features like parallel computing implementation and sensitivity analysis modules. Future research will continue exploring this domain to further refine multidisciplinary collaborative optimization methods and extend their application to broader problem domains including aerospace systems and automotive design optimization.