## 3 Results and Discussion
### Comparative Analysis of She Ethnic Clothing Classification  
#### 3.3.1 Comparative Analysis of Parameter Optimization Methods  
To address the subjectivity issue in manually determining parameter combinations, intelligent optimization algorithms are integrated into the parameter optimization process, considering the specific application scenarios. Common intelligent optimization algorithms include the Flower Pollination Algorithm (FPA) [43; 44] and Particle Swarm Optimization (PSO) [45], the Bat algorithm (BA) [46], Harmony Search (HS) [47], and the Artificial Bee Colony (ABC) [48]. Compared to these algorithms, the FPA has advantages such as fewer parameters, better stability, and less susceptibility to premature convergence. The FPA also exhibits excellent global and local search capabilities, achieving high optimization efficiency [49]. The QFPA is an improved algorithm based on the FPA by [PERSON] et al. in 2022 [50].  
To validate the effectiveness of the FPA, a comparison experiment was conducted on the minimum values of the second-order function using the FPA and QFPA, PSO, BA, HS, and ABC algorithms, respectively, as shown in Equation (12).  
\[F=-20 exp\left(-0.2\sqrt{\frac{1}{2}{\sum\limits_{i=1}^{2}{x_{i}^{2}}}}\right) -exp\left(\frac{1}{2}{\sum\limits_{i=1}^{2}{\cos(2\pi x_{i})}}\right)+e+20 \tag{12}\]  
where \(F\) represents the objective function, which is a second-order function, \(x_{i}\) represents the independent variables, specifically \(x_{1}\) and \(x_{2}\), and \(e\) represents the natural constant.  
Algorithm 1 presents the pseudo code of the FPA; the basis of the FPA relies on both local and global pollination mechanisms. Global pollination occurs in larger areas through biotic factors; abiotic factors enable the occurrence of local pollination in more limited areas.  
```
Objective min or max \(f(X)\), \(X=(x_{1},x_{2},\ldots,x_{d})\) Initialize a population of \(n\) flowers/ pollen gametes with random solutions Find the best solution \(g_{*}\) in the initial population Define a switch probability \(p\in[0,1]\) While (\(t<MaxGeneration\)) for i = 1:\(n\) (all \(n\) flowers in the population) if rand < \(p\), Draw a (d-dimensional) step vector L which obeys a Levy distribution Global pollination via \(X_{i}^{t+1}=X_{i}^{t}+L(g_{*}-X_{i}^{t})\) else Draw \(\epsilon\) from a uniform distribution in \([0,1]\) Randomly choose j and k among all the solutions Do local pollination via \(X_{i}^{t+1}=X_{i}^{t}+\epsilon(X_{j}^{t}-X_{k}^{t})\) end if Evaluate new solutions If new solutions are better, update them in the population end for Find the current best solution \(g_{*}\) end while
```  
**Algorithm 1** Pseudo code of FPA [13]  
Figure (a)a shows the optimization process of the FPA, QFPA, PSO, BA, HS, and ABC algorithms. Figure (b)b presents the optimization result obtained by the FPA. As shown in Figure (a)a, compared with the PSO, BA, HS, and ABC algorithms, the FPA and QFPA have faster optimization speeds. Compared with the QFPA, the FPA has a slower optimization speed, but its optimization results are closer to the global optimal. After 38 iterations, the FPA finds the minimum value of 0 and maintains it throughout the remaining iterations. The optimal solution obtained with the FPA is highly accurate, with coordinates preciseup to nine decimal places, indicating its ability to approximate the global minimum more effectively. Therefore, this paper adopts the FPA for She ethnic clothing color feature fusion.