二维：
import numpy as np import matplotlib.pyplot as plt pic_names = ["pic1", "pic2", "pic3", "pic4", "pic5", "pic6", "pic7", "pic8", "pic9", "pic10"] # 生成随机的数据 def generate_pareto_front(num_points=100): x = np.random.rand(num_points) y = np.random.rand(num_points) return x, y # 创建图形 fig, axs = plt.subplots(3, 4, figsize=(20, 15)) # 遍历基准函数并绘制 for i, name in enumerate(pic_names): if i < 8: # 前8个图 ax = axs[i // 4, i % 4] else: # 最后两个图 ax = axs[2, i - 8 + 1] # 移动到第三行的第2和第3个位置 # 生成并绘制随机数据 x, y = generate_pareto_front() ax.scatter(x, y, c='b', marker='o') ax.set_title(name.upper()) ax.set_xlabel("f1") ax.set_ylabel("f2") # 删除空白子图 fig.delaxes(axs[2, 0]) fig.delaxes(axs[2, 3]) # 调整布局 plt.tight_layout() plt.subplots_adjust(wspace=0.3, hspace=0.3) # 添加统一图例 handles, labels = axs[0, 0].get_legend_handles_labels() fig.legend(handles, labels, loc='center left', bbox_to_anchor=(0.8, 0.2), ncol=1,prop={'size': 18}) # 显示图形 plt.show()
三维：
import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D pic_names = ["pic1", "pic2", "pic3", "pic4", "pic5", "pic6", "pic7", "pic8", "pic9", "pic10"] # 生成随机的帕累托前沿数据 def generate_pareto_front(num_points=100): x = np.random.rand(num_points) y = np.random.rand(num_points) z = np.random.rand(num_points) return x, y, z # 创建图形 fig = plt.figure(figsize=(20, 15)) gs = fig.add_gridspec(3, 4) # 遍历基准函数并绘制 for i, name in enumerate(pic_names): if i < 8: # 前两行的8个图像 ax = fig.add_subplot(gs[i // 4, i % 4], projection='3d') elif i == 8: # 第9个图，居中显示 ax = fig.add_subplot(gs[2, 1], projection='3d') else: # 第10个图，居中显示 ax = fig.add_subplot(gs[2, 2], projection='3d') # 生成并绘制随机数据 x, y, z = generate_pareto_front() ax.scatter(x, y, z, c='b', marker='o') ax.set_title(problem_name.upper()) ax.set_xlabel("f1") ax.set_ylabel("f2") ax.set_zlabel("f3") # 设置视角 ax.view_init(elev=20, azim=20) # 删除空白子图 fig.delaxes(fig.add_subplot(gs[2, 0])) fig.delaxes(fig.add_subplot(gs[2, 3])) # 调整布局 plt.tight_layout() plt.subplots_adjust(wspace=0.3, hspace=0.3) # 显示图形 plt.show()
