## 1 Introduction  
The optimization of shape and topology is one of the areas of rapid growth in the automotive industry. Many practical applications of sophisticated optimization algorithms have been published in the last years, such as the SIMP method [1], which uses relative densities of the material as design variables (therefore, the name solid isotropic material with penalization). Method dealing with similar limit load optimization problems utilizing the SIMP method has been described in [2], highlighting the drawbacks of using this scheme for buckling prediction problems. Another method called bi-directional evolutionary structural optimization (BESO) has been used in these applications, in accordance with [3]. One of the most recently developed methods for structural optimization is the level-set method (LSM), broadly described in [4]. The LSM does not use element-based densities as design variables; however, it changes the shape (the boundary) of the body based on the auxiliary field. Most of the popularized methods do not require explicit parametrization; therefore, their solution does not require the user to specify many parameters of the algorithm. On the other hand, parametric optimization, in which the parameters (variables) represent physical quantities, such as dimensions, material properties, loads, etc., requires a detailed definition of the optimization algorithm parameters. This may be seen especially in genetic algorithms, where population size (number of islands, number of individuals per island, number of generations) may be adjusted freely by the designer, in addition to the probabilities of migration, mutation, crossover, elite size, etc. Different combinations of the aforementioned algorithm parameters may show a broad scatter in the convergence rate or the final fitness function (objective function) during optimization. Evolutionary optimization is still an actively developing field. In [5], the authors proposed a modified version of the genetic algorithm, in which the genetic operators are carried out independently on each generation, i.e., crossover is performed during every 2n generations, while the mutation during every 2n + 1 generations. The authors utilized a large number of individuals in the population combined with only a few generations. The great extinction genetic algorithm (GEGA) modification proposed in [6] incorporated nature-based phenomena, such as global disasters to remove part of the individuals from the population, which are later randomly replaced by new individuals. The authors focused on minimizing the number of objective function calls, which may be treated as a method to mitigate one of the major drawbacks of GA--excessive computational effort associated with simulation-based objective function evaluation. To capture the actual global optimum with limited resources, the gradient-based optimization procedure was incorporated after the evolutionary optimization. With a similar amount of individuals, yet utilized in different populations, the authors of [7] were able to optimize the automotive suspension layout with a limited number of generations and small population sizes in the Multi-Island version of the genetic algorithm (MIGA). A recent review of genetic algorithms [8] points out that the foreseen progress of GAs should cover the tuning of appropriate degrees of crossover and mutation operators. In addition, the hybridization of algorithms is marked as one of the potential solutions to the overly-intensified objective function calculations associated with the GAs. A combination of global-search GA with local-search techniques proves to provide an efficient optimization tool, assuring the balance between diversification and computational effort. The authors of [8] provide numerous references supporting the efficiency of this hybrid approach in numerous fields of science. In general, different algorithms can be joined sequentially or parallelly, making a hybrid optimization scheme.