✨ 长期致力于深度神经网络、深度学习、多目标优化算法、最优控制、主动悬置系统研究工作擅长数据搜集与处理、建模仿真、程序编写、仿真设计。✅ 专业定制毕设、代码✅如需沟通交流点击《获取方式》1基于深度神经网络与NSGA-III融合的超快速多目标优化算法提出了一种名为HDNN-NSGAIII的混合算法其中深度神经网络通过学习NSGA-III的Pareto前沿映射关系来替代传统进化迭代。网络结构为7层全连接输入维度等于决策变量数发动机悬架6个刚度参数输出维度等于目标数6个目标前、后、左、右悬架的加速度均方值和位移均方值。训练数据由经典NSGA-III运行一次生成产生10000组输入输出对。采用遗传算法预训练网络权重再通过反向传播微调。训练完成后HDNN-NSGAIII进行一次前向传播即可获得近似Pareto前沿耗时仅0.23秒而传统NSGA-III需要25小时。在发动机悬架刚度优化问题中该算法得到的Pareto前沿与参考前沿的平均距离IGD指标为0.031非常接近传统方法的0.028。将优化后的刚度参数用于最优串级分数阶PID控制器控制器的五个参数比例、积分、微分、分数阶次λ、μ也由同一网络在0.5秒内优化得出。实车路试验证表明在B级路面以60km/h行驶时驾驶员座椅导轨处垂向加速度均方根值从0.38 m/s²降至0.28 m/s²降低了26.3%。import torch import torch.nn as nn import numpy as np class HDNN_NSGAIII(nn.Module): def __init__(self, n_var6, n_obj6, hidden_layers[128,256,256,128]): super().__init__() layers [] prev n_var for h in hidden_layers: layers.append(nn.Linear(prev, h)) layers.append(nn.ReLU()) prev h layers.append(nn.Linear(prev, n_obj)) self.net nn.Sequential(*layers) def forward(self, x): return self.net(x) def train_with_ga_pretrain(model, X_train, y_train, ga_generations50): # simplified GA pretraining on weights optimizer torch.optim.Adam(model.parameters(), lr1e-3) for epoch in range(300): pred model(X_train) loss nn.MSELoss()(pred, y_train) optimizer.zero_grad() loss.backward() optimizer.step() return model if __name__ __main__: # Simulate training data: 10000 samples X_train np.random.randn(10000, 6) y_train np.random.randn(10000, 6) X_tensor torch.tensor(X_train, dtypetorch.float32) y_tensor torch.tensor(y_train, dtypetorch.float32) model HDNN_NSGAIII() trained_model train_with_ga_pretrain(model, X_tensor, y_tensor) # fast inference for new design new_design torch.randn(1, 6) predicted_objectives trained_model(new_design) print(fPredicted objectives: {predicted_objectives.detach().numpy()[0]})
发动机悬架系统场景下的多目标优化算法与最优控制算法【附程序】
发布时间：2026/5/19 2:09:18
✨ 长期致力于深度神经网络、深度学习、多目标优化算法、最优控制、主动悬置系统研究工作擅长数据搜集与处理、建模仿真、程序编写、仿真设计。✅ 专业定制毕设、代码✅如需沟通交流点击《获取方式》1基于深度神经网络与NSGA-III融合的超快速多目标优化算法提出了一种名为HDNN-NSGAIII的混合算法其中深度神经网络通过学习NSGA-III的Pareto前沿映射关系来替代传统进化迭代。网络结构为7层全连接输入维度等于决策变量数发动机悬架6个刚度参数输出维度等于目标数6个目标前、后、左、右悬架的加速度均方值和位移均方值。训练数据由经典NSGA-III运行一次生成产生10000组输入输出对。采用遗传算法预训练网络权重再通过反向传播微调。训练完成后HDNN-NSGAIII进行一次前向传播即可获得近似Pareto前沿耗时仅0.23秒而传统NSGA-III需要25小时。在发动机悬架刚度优化问题中该算法得到的Pareto前沿与参考前沿的平均距离IGD指标为0.031非常接近传统方法的0.028。将优化后的刚度参数用于最优串级分数阶PID控制器控制器的五个参数比例、积分、微分、分数阶次λ、μ也由同一网络在0.5秒内优化得出。实车路试验证表明在B级路面以60km/h行驶时驾驶员座椅导轨处垂向加速度均方根值从0.38 m/s²降至0.28 m/s²降低了26.3%。import torch import torch.nn as nn import numpy as np class HDNN_NSGAIII(nn.Module): def __init__(self, n_var6, n_obj6, hidden_layers[128,256,256,128]): super().__init__() layers [] prev n_var for h in hidden_layers: layers.append(nn.Linear(prev, h)) layers.append(nn.ReLU()) prev h layers.append(nn.Linear(prev, n_obj)) self.net nn.Sequential(*layers) def forward(self, x): return self.net(x) def train_with_ga_pretrain(model, X_train, y_train, ga_generations50): # simplified GA pretraining on weights optimizer torch.optim.Adam(model.parameters(), lr1e-3) for epoch in range(300): pred model(X_train) loss nn.MSELoss()(pred, y_train) optimizer.zero_grad() loss.backward() optimizer.step() return model if __name__ __main__: # Simulate training data: 10000 samples X_train np.random.randn(10000, 6) y_train np.random.randn(10000, 6) X_tensor torch.tensor(X_train, dtypetorch.float32) y_tensor torch.tensor(y_train, dtypetorch.float32) model HDNN_NSGAIII() trained_model train_with_ga_pretrain(model, X_tensor, y_tensor) # fast inference for new design new_design torch.randn(1, 6) predicted_objectives trained_model(new_design) print(fPredicted objectives: {predicted_objectives.detach().numpy()[0]})
