# Pedestrian Comfort Analysis and Parameterized Optimization Design for Raised Floors
## 3 Results
### Optimized Design
#### 3.3.2 Optimize Process  
This study conducted a joint optimization analysis using the Isight software in conjunction with Abaqus. The Isight optimization platform offers unique advantages, enabling automatic parameter adjustment and calculation. It provides a variety of experimental design options and can automatically design experiments based on requirements. The software's parallel computing capabilities maximize computing resources and improve the efficiency of the entire analysis process. Furthermore, Isight offers powerful post-processing functions to enhance the visual representation of results. The approach taken in this paper to optimize the comfort of walking on raised floors was to calculate the first-order vertical natural frequencies and peak accelerations for different sizes of optimization variables, in order to determine the final optimized dimensions. Figure 11 illustrates the optimization design process adopted in this paper. The Isight software leverages its built-in algorithms to create models of various components within a raised floor system and conducts finite element analysis for parametric exploration. Due to the complex nature of a raised floor assembly, a Matlab module was employed to validate the data produced by Isight, ensuring consistency in the assembly conditions of the components and providing model dimensions that adhered to the operational requirements of Abaqus upon regeneration. The Abaqus module then conducted finite element analysis on the refined model, yielding crucial information such as the total thickness of the support plate layer and the displacement-time curve of the most critical point. The Simcode module, consisting of custom-written programs, calculated the second derivative of the deformation-time curve derived from Abaqus. This calculation revealed the peak acceleration value in the most susceptible vibration zone under walking-induced excitation. The code used for this calculation is provided in the Supplementary Material. By seamlessly integrating Matlab, Abaqus, and Simcode, Isight facilitated a comprehensive comfort analysis of the raised floor system under walking excitation. Following the optimization process, the results were meticulously scrutinized to ascertain if the first-order vertical resonance frequency met the stipulated criteria. If deemed feasible, the product underwent prototyping; conversely, any shortcomings in the results prompted a thorough examination to identify areas for further optimization and improvement. Figure 11: Model optimization process. In order to improve computational efficiency, a simplified analysis of walking loads was conducted prior to optimization analysis. To balance efficiency and accuracy, the triangular wave model for pedestrian loading excitation from Ref. [34] was employed. The specific calculation formula is presented in Equation (12), which excludes dynamic coefficients and phase parameters for ease of computational implementation. \[\left\{ \begin{matrix}F=(1.2/(1/2f))(t-i\cdot(1/f))\\ F=-(1.2/(1/2f))\left[t-(i+1)\cdot(1/f)\right]\end{matrix} \right.i\in Z,i\geq 0 \tag{12}\]  
where \(f\) is step frequency; \(t\) is walking time; \(F\) is pedestrian load (normalized by body weight G); and \(i\) is an integer. This study utilized a multi-island genetic algorithm (MIGA) for optimization calculations, which is inspired by the genetic reproduction mechanism in biological evolution processes. It involves the encoding of individuals in the solution space, followed by genetic operations on the encoded population through iterations to obtain optimal or near-optimal solutions. Compared to traditional genetic algorithms (GAs), MIGA demonstrates a superior global optimization capability and computational efficiency.