Optimum design of a seat bracket using artificial neural networks and dandelion optimization algorithm

Nature-inspired metaheuristic algorithms are gaining popularity with their easy applicability and ability to avoid local optimum points, and they are spreading to wide application areas. Meta-heuristic optimization algorithms are used to achieve an optimum design in engineering problems aiming to obtain lightweight designs. In this article, structural optimization methods are used in the process of achieving the optimum design of a seat bracket. As a result of topology optimization, a new concept design of the bracket was created and used in shape optimization. In the shape optimization, the mass and stress values obtained depending on the variables, constraint, and objective functions were created by using artificial neural networks. The optimization problem based on mass minimization is solved by applying the dandelion optimization algorithm and verified by finite element analysis.