## 3 Optimization Models  
### Crested Porcupine Optimizer (CPO) Optimization  
The Support Vector Machine (SVM) was proposed by [PERSON] and Vapnik in 1995 and shows many unique advantages in solving small samples and in nonlinear and high \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Engine Speed** & \multirow{2}{*}{**Torque (N-m)**} & \multirow{2}{*}{**Inlet NOx (ppm)**} & \multicolumn{2}{c}{**SCR Inlet**} & \multicolumn{2}{c}{**Exhaust Flow**} & \multicolumn{2}{c}{**Urea Injection**} \\
**(ppm)** & & & **Temperature (°C)** & **(kg/h)** & **(mg/s)** \\ \hline
1200 & 75 & 550 & 264 & 86 & 50.4 \\
1200 & 175 & 1110 & 287 & 144 & 170.2 \\
1200 & 275 & 1700 & 355 & 166 & 300.5 \\
1200 & 375 & 1980 & 417 & 191 & 402.6 \\
1200 & 475 & 1800 & 472 & 223 & 427.3 \\
1800 & 50 & 400 & 256 & 126 & 53.7 \\
1800 & 100 & 543 & 259 & 185 & 107.0 \\
1800 & 200 & 864 & 267 & 295 & 271.4 \\
1800 & 300 & 1266 & 315 & 344 & 463.7 \\
1800 & 400 & 1668 & 378 & 381 & 676.6 \\
1800 & 500 & 1598 & 494 & 395 & 672.0 \\
2400 & 50 & 381 & 259 & 223 & 90.5 \\
2400 & 150 & 780 & 296 & 328 & 272.4 \\
2400 & 250 & 1066 & 295 & 502 & 569.8 \\
2400 & 350 & 1402 & 396 & 516 & 770.2 \\
2400 & 450 & 1315 & 521 & 526 & 736.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Engine performance and emission parameters. Figure 1: Original engine emission parameters. -dimensional pattern recognition, and it can be generalized to other machine learning problems such as function fitting [23]. In this study, for each working condition point there are 25 sets of experimental data. Therefore, in the case of fewer data samples, it is obvious that SVM is more advantageous compared to other machine learning methods such as neural networks. Most machine learning algorithms have a number of parameters that can be tuned, called hyperparameters. Hyperparameters are very important in building effective and accurate models. They help us find a balance between bias and variance, thus preventing the model from overfitting or underfitting. In order to improve the predictive accuracy of SVM models, it is necessary to find the optimal hyperparameter penalty coefficient C, as well as the kernel function coefficient gamma. The Crested Porcupine Optimizer (CPO) algorithm was proposed by [PERSON] and, along with the Particle Swarm Optimization (PSO) algorithm, is a population-based algorithm. Population-based algorithms have advantages over single-based algorithms due to their strong search space and global optimum. In addition, the core advantage of CPO and PSO optimization of SVM is its global search capability, efficiency and ease of use, and it is especially suitable for complex kernel function parameter optimization and small sample scenarios. Its robustness can be further enhanced when combined with hybrid strategies or adaptive improvement. Therefore, it will be combined with genetic algorithm in the subsequent optimization process to avoid overfitting. However, the CPO algorithm is more effective than its counterparts in terms of average fitness, standard deviation, convergence curve and computational cost [24]. Therefore, in this paper, the CPO algorithm is used to optimize the hyperparameters of the SVM model and the optimization results are compared with those of the PSO algorithm. The CPO optimization algorithm combines exploration and exploitation mechanisms by visualizing the search space. The first two of these defense strategies (vision and sound) correspond to exploratory behaviors, and the last two (odor and physical attack) correspond to exploitative behaviors. The algorithm is similar to other population metaheuristics in that the search is initiated from an initial set of candidate solutions and the quality of individuals is evaluated by an objective function. The optimization problem consists of multiple dimensions, all of which need to be applied to minimize or maximize. The CPO algorithm begins by randomly initializing the dimensions within the search space of each problem, as shown in Equation (1).