## 1 Introduction
[15] proposed a two-stage method for the fixture layout of sheet metal to reduce the deflection of sheet metal normal to surface. This method is applied to the rear bracket plate and results showed it has higher prediction accuracy and good simulation effect. [PERSON] et al. [16] proposed the N-2-1 locating scheme for the positional variation analysis of sheet metal workpieces. [PERSON] et al. [17] proposed a new N-3-2-1 method for sheet metal fixture layout optimization and found the best optimal position for the multipoint respot welding of sheet metals. Optimization is performed using GA. The spacer grid case study is performed to validate results. Different techniques have been used to reduce computational effort in fixture layout optimization. [PERSON] et al. [18] proposed three methods: adaptive, exponential, and linear reduction methods, to reduce population size for GA. It was found that the adaptive method outperformed the other two methods. Besides GA, some other optimization methods are also used in structural optimization. [PERSON] and [PERSON] [19] developed the Big Bang-Big Crunch (BB-BC) algorithm for truss structure optimization. Authors compared the BB-BC algorithm to other optimization algorithms including the ant colony algorithm, GA, particle swarm optimization (PSO), and harmony search. Numerical results indicate that the BB-BC algorithm has better performance than other heuristic algorithms. [PERSON] et al. [20] adopted a graphical method and PSO to produce optimal origami patterns. Numerical experiments were performed and it was concluded that the proposed PSO method results in high accuracy, as well as low computational cost. [PERSON] and [PERSON] [21] proposed the Democratic PSO algorithm to overcome premature convergence phenomena in the original PSO. The proposed algorithm is validated by solving four numerical problems. [PERSON] et al. [22] proposed the PSO for developing flat-foldable origami patterns with degree -4 vertices. Authors concluded that the presented method is better than the analytical approach and GA because both trivial and non-trivial flat-foldable solutions are found using less computational effort. [PERSON] et al. [1] identified the symmetry order and symmetry group of planar structures using two Convolutional Neural Networks (CNNs). A short summary of currently existing methods, characteristics, and limitations are described in Table 1.  
In this research, the topology optimization technique is used to reduce the population for GA. Topology optimization is generally applied in the early stages of design. The main purpose of topology optimization is to get an optimized structured layout. In topology optimization some material is removed and a new layout is obtained [29]. At present, different FEM softwares (Altair Optistruct, MSC Nastran, Simulia Tosca, etc.) are available that provide an optimization module to obtain lighter-weight structures. However, different researchers have developed [22; 23] their own codes using programming languages. General two methods are used for the topology optimization method: the homogenization method and density method. [PERSON] and [PERSON] [30] proposed the homogenization method, but it is computationally expensive. To overcome this issue later, a density method was proposed by [PERSON] [31] and called the solid isotropic material with penalization method (SIMP); it was further developed by [PERSON] and [PERSON] [32]. [PERSON] [33] solved two-dimensional problems considering the displacement-constrained, volume-minimizing topology optimization model. The density method considers isotropic material [34]. The MSC Nastran solver is based on the density method. [PERSON] et al. [35] proposed a method for the topology optimization of nonlinear response using equivalent static loads (ESL). ESL are calculated from the analysis domain results. These calculated loads are used in linear response optimization. [PERSON] et al. [36] performed topology optimization by considering stress constraints in 3D models with regards to the SIMP method.