# Optimization of Composite Structures with Thin Rigid Fibers Using Bioinspired Algorithms
## 6 Conclusions  
An effective tool for the bioinspired optimization of composites with rigid fibers has been presented. Two different optimization algorithms were used: the artificial immune system (AIS) and the particle swarm optimizer (PSO). The aim of using two independent algorithms was to check whether the obtained solutions were optimal. The boundary element method was used to analyze these types of composites. Several numerical examples for the variants of location, orientation, and the length of fibers, and two types of boundary conditions have been presented. In the first example, the square plate was simply supported and uniformly loaded by traction in the horizontal direction. The decision variables for this example determined the orientation of the fibers described by five parameters. In this example, the elastic Young's modulus and effective Poisson's ratio were optimized (maximization and minimization). For the second example, the loading scheme was changed. In this example, the square plate was simply supported and loaded by shear traction. In this example, the effective shear modulus was optimized (maximization and minimization). In the third example, the geometrical parameters were the same as in the previous examples. The square plate was simply supported and uniformly loaded by traction in the horizontal direction. The difference was the number of fibers and the possibility of their modification. A square plate contained 18 fibers in five columns. The decision variables for this example determined the distribution of the fibers. The aim of optimization was to minimize or maximize the effective elastic Young's modulus in the direction of loading. In the last example, two additional design variable were added. The purpose of this modification was to find a better solution than the previous ones. The aim of optimization was to maximize the Young's modulus. The decision variables determined the location of the fibers. The last decision variable determined the fibers' length. Based on the obtained results, it can be concluded that the implementation of this approach utilizing AIS and PSO provides a high probability of finding the global optimal solutions. Two independent bioinspired algorithms were used to confirm the correctness of the obtained results. Additionally, each optimization was performed ten times to ensure that the same results were obtained. For well-set AIS and PSO parameters, the same results were obtained each time. Based on the examples included in the article, it can be stated that both algorithms were effective, and the efficiency of both algorithms was at a similar level. The proposed method involving the use of AIS/PSO and BEM allows us to design new composites. Unfortunately, one of the limitations of this work when modeling composites was the lack of possibility of fiber intersection. Further improvements in the modeling of composites are possible with the utilization of intersecting fibers. In this way, it will be possible to model more realistic versions of composites. There are also  
Figure 15: The best result after the optimization process: the optimal distribution of the rigid fibers. possibilities of achieving efficiency improvements with the proposed method, e.g., by the application of hybrid bioinspired methods [20]. In this article, numerical calculations were performed for which there is currently no comparison with experimental counterparts. The comparison of numerical results with experiments is planned in the future to compare numerical simulations with real-life results. However, the validation of the obtained results was performed for two calculation methods, i.e., for BEM and FEM. The results obtained with both methods for this type of composite were the same, but, among others, due to a much smaller number of elements (only the boundary discretization for BEM), the BEM method turned out to be faster and was selected for optimization [39]. Additionally, the optimization process was validated using two different algorithms: first AIS and next PSO.