## 4 Discussion
The traditional algorithms do not provide the initial parameters near to the optimal parameters. It is because the traditional algorithms do not determine the initial values by solving the model via surface data. Furthermore, the proposed metaheuristic algorithm performs exploration and exploitation, which perform a search procedure inside and outside of parents. Thus, all search space can be analyzed. This procedure leads to finding the best solution for the model parameters in an efficient form. The capability of the proposed metaheuristic genetic algorithm is elucidated by comparison with respect to the metaheuristic algorithms. To elucidate this, the quality gap, running time, and suitable structure of the metaheuristic algorithms are mentioned as follows. The particle swarm optimization provides a quality gap of 4.44% and a running time of 253 generations for spherical and cylindrical surface modeling. The ant colony optimization provides a quality gap of 4.74% and a running time of 286 generations. The simulated annealing optimization provides a quality gap of 4.91% and a running time of 292 generations. The suitable structure is compared with the particle swarm optimization that provides better results. To do so, the population evolution of the particle swarm is described as follows. The population evolution of the particle swarm is described by the expressions \(V_{i}(t+1)=wV_{i}(t)+aR_{1}[P^{b}{}_{g}(t)-P_{i}(t)]+\beta R_{2}[P^{b}{}_{i}(t )-P_{i}(t)]\), where \(P_{i}(t+1)=P_{i}(t)+V_{i}(t)\), where \(t\) is the number of iterations, and \(\alpha\) and \(\beta\) represent the learning factors. The parameters \(R_{1}\) and \(R_{2}\) are random numbers ranging from 0 to 1, and \(w\) is the inertia weight. Based on these statements, the population of each generation is generated by a function that depends on five variables, which should be determined. The genetic algorithm determines the population of each generation by a function that depends on the variable \(\beta\), which is computed via the spread factor between 0 and 1. Therefore, the genetic algorithm performs a structure that is more simple than the particle swarm optimization. In addition, ant colony optimization and simulated annealing optimization employ more variables than the genetic algorithm to construct surface models. From the results, the genetic algorithm elucidates a suitable structure to construct surface models. This statement is corroborated by the surface model constructed via genetic algorithm and by particle swarm optimization [67]. Based on these statements, the capability of the micro-scale spherical and cylindrical surface modeling via the metaheuristic algorithm based on the micro laser line projection is elucidated. Moreover, the low cost of the simple setup increases the viability of the proposed micro-scale spherical and cylindrical surface modeling. In this way, the micro-scale surface modeling via the genetic algorithm and micro laser line projection provides a contribution for the optical microscope imaging systems, which perform micro-scale surface modeling. The computer employed to perform the micro-scale spherical and cylindrical surface modeling is a PC to 1.8 GHz of velocity. The frame rate of the camera is 58 fps. The slider device moves the microscope vision system via control software. Each micro laser line image is processed in 0.0074 s to determine the micro-scale surface contour. The spherical surface model for the plastic surface is determined in 26.21 s. The time of this procedure includes the surface scanning and the surface modeling. The cylindrical surface model for the metallic surface is determined in 25.48 s.