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.

优化是最小化或最大化功能，获得成本，质量，能量，收益等方面最佳结果的必要工具。实际问题可能是多峰，非线性和不连续的，并且可能无法通过依赖于梯度的经典分析方法来最小化。在这种情况下，有一些受自然现象启发的元启发式算法可以优化实际的工程问题。没有算法是最差或最好的，但是对于给定的问题，效率更高。因此，本研究中采用了一种新的受自然启发的算法，称为Lichtenberg优化算法（LA），以解决复合材料建造的机械结构中的复杂逆损伤识别问题。为了验证新算法的性能，LA和有限元方法（FEM）均用于识别分层损伤，并将结果与​​其他算法（如遗传算法（GA）和SunFlower优化（SFO））进行比较。LA被证明是强大的损害识别工具，因为它即使在嘈杂的响应和较低的损害严重性等特殊情况下也能够检测到损害。