A powerful Lichtenberg Optimization Algorithm: A damage identification case study. (January 2021) - Pereira, João Luiz Junho; Francisco, Matheus Brendon; Cunha Jr., Sebastião Simões da; Gomes, Guilherme Ferreira
Abstract: Optimization is an essential tool to minimize or maximize functions, obtaining optimal results on costs, mass, energy, gains, among others. Actual problems may be multimodal, nonlinear, and discontinuous and may not be minimized by classical analytical methods that depend on the gradient. In this context, there are metaheuristic algorithms inspired by natural phenomena to optimize real engineering problems. No algorithm is the worst or the best, but more efficient for a given problem. Thus, a new nature-inspired algorithm called Lichtenberg Optimization Algorithm (LA) is applied in this study to solve a complex inverse damage identification problem in mechanical structures built by composite material. To verify the performance of the new algorithm, both LA and Finite Element Method (FEM) were used to identify delamination damage and the results were compared to other algorithms such as Genetic Algorithm (GA) and SunFlower Optimization (SFO). LA was shown to be a powerful damage identification tool since it was able to detect damage even in particular situations like noisy response and low damage severity.