Add figure caption

Author: BillHuang2001

docs/source/locale/zh_CN/tutorial/tutorial_part7.md

@@ -14,7 +14,13 @@ f(\mathbf{x}) = 10 d + \sum_{i=1}^{d}[x_i^2 - 10 \cos{(2\pi x_i)}],
 
 其中$\mathbf{x} \in \mathbb{R}^d$, $d$为该函数的维数。Rastrigin 函数的全局最优值为$0$，出现在原点。为了清晰地展示该函数具有多个局部极值点的性质，我们绘制了二维 Rastrigin 函数的图像。
 
-![Rastrigin Function](/_static/rastrigin_function.svg)
+```{figure} /_static/rastrigin_function.svg
+:alt: A plot of the Rastrigin function
+:figwidth: 70%
+:align: center
+
+Rastrigin 函数
+```
 
 在本案例中，我们将使用粒子群优化（PSO）算法在十维 Rastrigin 函数上寻优。
 
@@ -212,7 +218,13 @@ plt.show()
 
 我们可以得到多次迭代后的种群分布情况，实验结果同样符合我们的预期：
 
-![Example NSGA-II Population](/_static/example_nsga2_result.svg)
+```{figure} /_static/example_nsga2_result.svg
+:alt: A plot of the NSGA-II population
+:figwidth: 70%
+:align: center
+
+NSGA-II算法优化后的种群分布
+```
 
 同时，在 JupyterNotebook 中，您还可以通过 EvoX 的可视化模块直接得到动态的实验结果，您可以直观地看到种群是如何随着算法的迭代更新的。为了实现这一目标，您只需要运行一行代码：
 

docs/source/tutorial/tutorial_part7.md

@@ -14,7 +14,13 @@ f(\mathbf{x}) = 10 d + \sum_{i=1}^{d}[x_i^2 - 10 \cos{(2\pi x_i)}],
 
 where $\mathbf{x} \in \mathbb{R}^d$ and $d$ is the dimensionality. The global optimum is 0 at the origin. The function is highly multimodal, making it ideal for testing global optimization algorithms. Here's a plot of the Rastrigin function
 
-![Rastrigin Function](/_static/rastrigin_function.svg)
+```{figure} /_static/rastrigin_function.svg
+:alt: A plot of the Rastrigin function
+:figwidth: 70%
+:align: center
+
+Rastrigin function
+```
 
 In this example, we will use the Particle Swarm Optimization (PSO) algorithm to optimize the 10-dimensional Rastrigin function.
 
@@ -185,7 +191,13 @@ plt.show()
 
 We can visualize the results using Matplotlib. The blue points represent the optimized population, while the red line shows the Pareto front.
 
-![Example NSGA-II Population](/_static/example_nsga2_result.svg)
+```{figure} /_static/example_nsga2_result.svg
+:alt: A plot of the NSGA-II population
+:figwidth: 70%
+:align: center
+
+A plot of the NSGA-II population after optimization
+```
 
 In Jupyter Notebook, you can use EvoX's built-in plotting capabilities to visualize the optimization process and monitor how the population evolves over generations.