Multi-objective parametric optimization of a composite high-performance prostheses using metaheuristic algorithms

With the rapid advancement of technology and computing, numerous processes and products have been developed to achieve improved performance at a lower cost and/or reduced material usage. Orthopedic prostheses, benefiting from the discovery of new materials, have witnessed continuous evolution and optimization. Therefore, this study aims to develop and parametrically optimize a high-performance composite material using the interpolation strategy of splines and keypoints, thickness variations, and the number of layers, resulting in 16 decision variables. Furthermore, the performance of four different multi-objective optimizers was evaluated in the optimization process: NSGA-II, MOLA, MOSFO, and MOPSO. The study aimed to minimize the total mass and evaluate the Tsai–Wu failure criterion under two different loading conditions. The numerical results obtained through the finite element method and optimization led to different convex Pareto fronts. The MOPSO algorithm demonstrated superior robustness compared to the other metaheuristic algorithms evaluated. As a result, the optimal solution obtained using MOPSO was substantially improved compared to the initial model, indicating the effectiveness of this approach in optimizing the high-performance composite material for the specific problem under consideration.