Tuning metaheuristic algorithms using mixture design: Application of sunflower optimization for structural damage identification. (November 2020) - Gomes, Guilherme Ferreira; de Almeida, Fabricio Alves
Highlights: An efficient metaheuristic optimization algorithm (SFO) is developed for damage identification of plate-like composite structures. Biological operators of SFO are optimized with the statistical method of mixture design. The improved SFO algorithm outperforms GA and requires less structural analyses to complete the identification process. The proposed approach allows structural damage to be identified more effectively. Graphical abstract: Abstract: This paper presents an efficient inverse global optimization approach for damage identification of plate-like structures. In this approach, the damage identification process is performed by minimizing an objective function based on modal parameters of CFRP laminated structures. The identification process entails two steps: i) the direct problem is modeled using the finite element method. Damage is induced into the two different situations, first as a variation in physical properties, i.e., delamination, as a variation in stiffness and also as a variation in the grommet properties, for example small circular holes; ii) For solving the optimization problem, an enhanced SunFlower Optimization (SFO) algorithm is applied in the inverse problem methodology. The SFO metaheuristic algorithm has its biological operators optimized by mixture design method. The efficiency of the proposed identification is investigated through two numerical examples for laminated composite plates where Genetic Algorithm, SFO and an improved SFO algorithm are compared. The obtained results indicate that the proposed Structural Health Monitoring method can successfully identify the location and the severity of small induced damage cases in the laminated composite plate. In addition, the improved algorithm was shown to be more efficient and accurate than the widely known and applied Genetic Algorithm.