Click the blue text above to follow us
๐ Author’s Introduction: Graduate from a 985 university, researcher and developer in the field of MATLAB;
๐ Right inscription: In the hundred miles, half of it is in the nine and ten.
โ Research Scope: Complete code, paper reproduction, program customization, journal writing, research collaboration
๐ Code Acquisition Method 1:Method to obtain MATLAB Poseidon code
๐ Code Acquisition Method 2:
For more MATLAB optimization simulation content, click below๐
MATLAB Optimization (Advanced Version)
Introduction
๐๐๐ The table of contents is as follows: ๐๐๐
Table of Contents
๐ฅ1 Overview
๐2 Code Snippets and Execution Steps
๐3 Execution Results
๐4 MATLAB Version and References
๐5 Code Acquisition Methods
๐ 6 Simulation Consultation
1 Overview
**1 Principle** The Grey Wolf Optimization (GWO) algorithm mimics the social hierarchy and hunting behavior of grey wolves, guiding the pack to search for the optimal solution through ฮฑ, ฮฒ, and ฮด wolves, suitable for continuous space optimization problems. Variational Mode Decomposition (VMD) is an adaptive signal decomposition method that can decompose complex signals into multiple modal components. When combining GWO with VMD to optimize PID parameters, VMD is used to extract key features of the system response signal (such as overshoot and rise time), while GWO dynamically adjusts the PID parameters (Kp, Ki, Kd) based on these features, forming a closed-loop optimization process. The objective function is typically an error integral metric (such as ITSE), and parameter optimization is achieved by minimizing the objective function. 2 Process Implementation **Initialization Phase** Set GWO parameters: population size (usually 20-50), maximum number of iterations (50-200), search space (range of PID parameters). Initialize the positions of grey wolves, each position corresponding to a set of PID parameters (Kp, Ki, Kd). **VMD Signal Processing** Collect the system response signal (such as step response), use VMD to decompose the signal to obtain modal components. Select key modal components to calculate the objective function (such as ITSE): $$ ITSE = \int_0^\infty t \cdot e(t)^2 dt $$ where e(t) is the system error. **GWO Iterative Optimization** Calculate the fitness of each wolf (objective function value) and update the positions of ฮฑ, ฮฒ, and ฮด wolves. The position update formula is: $$ \vec{X}(t+1) = \frac{\vec{X}_1 + \vec{X}_2 + \vec{X}_3}{3} $$ $$ \vec{X}_i = \vec{X}_p – \vec{A} \cdot \vec{D}, \quad \vec{D} = |\vec{C} \cdot \vec{X}_p – \vec{X}| $$ where $\vec{A}$ and $\vec{C}$ are coefficient vectors, and $\vec{X}_p$ is the leader’s position. **Termination Condition** Terminate when the maximum number of iterations is reached or the change in fitness is less than a threshold, outputting the optimal PID parameter combination. Verify the control performance of the optimized parameters in the actual system. **3 Key Code Example (Python Pseudocode)**
# GWO core update logic def gwo_update(population, alpha, beta, delta): a = 2 - iter_num * (2 / max_iter) # Linear decay for wolf in population: A1, A2, A3 = 2*a*random() - a, 2*a*random() - a, 2*a*random() - a C1, C2, C3 = 2*random(), 2*random(), 2*random() D_alpha = abs(C1*alpha.pos - wolf.pos) D_beta = abs(C2*beta.pos - wolf.pos) D_delta = abs(C3*delta.pos - wolf.pos) X1 = alpha.pos - A1*D_alpha X2 = beta.pos - A2*D_beta X3 = delta.pos - A3*D_delta wolf.pos = (X1 + X2 + X3) / 3 # Position update return population # VMD signal processing (requires vmdpy library) from vmdpy import VMD def vmd_feature_extract(signal): alpha = 2000 # Bandwidth limit tau = 0 # Noise tolerance K = 3 # Number of modes DC = 0 # No DC component init = 1 # Initialize center frequency tol = 1e-7 # Tolerance error u, omega = VMD(signal, alpha, tau, K, DC, init, tol) return u[0] # Take the main modal component **4 Notes** – The number of modes K in VMD should be experimentally determined based on the characteristics of the signal – The GWO search range should cover a reasonable physical range of PID parameters – When implementing on actual hardware, consider the impact of sampling frequency and computational delay – Joint simulation verification can be conducted with MATLAB/Simulink
2 Code Snippets and Execution Steps
2.1 Code Snippets2.2 Execution Steps
(1) Directly run main.m to generate the plot with one click
3Execution Results
4MATLAB Version and References
1 MATLAB Version
2019b
2 References
[1] Hu Zhiyong, Long Zuqiang, Gu Jiaying, Sun Ke. Parameter Optimization of Fuzzy PID Controller Based on Grey Wolf Optimization Algorithm [J]. Automation and Information Engineering. 2025
3 Notes
This section is extracted from the internet for reference only. If there is any infringement, please contact for deletion.
5 Code Acquisition Methods
6 Simulation Consultation
**๐ Simulation Consultation**
1 Various intelligent optimization algorithm improvements and applications
1.1 PID Optimization
1.2 VMD Optimization
1.3 Distribution Network Reconstruction
1.4 3D Packing
1.5 Microgrid Optimization
1.6 Layout Optimization
1.7 Parameter Optimization
1.8 Cost Optimization
1.9 Charging Optimization
1.10 Scheduling Optimization
1.11 Price Optimization
1.12 Departure Optimization
1.13 Distribution Optimization
1.14 Coverage Optimization
1.15 Control Optimization
1.16 Inventory Optimization
1.17 Routing Optimization
1.18 Design Optimization
1.19 Location Optimization
1.20 Absorption Optimization
1.21 Site Selection Optimization
1.22 Operation Optimization
1.23 Assignment Optimization
1.24 Combination Optimization
1.25 Workshop Scheduling
1.26 Production Scheduling
1.27 Economic Scheduling
1.28 Assembly Line Scheduling
1.29 Reservoir Scheduling
1.30 Cargo Position Optimization
1.31 Bus Scheduling Optimization
1.32 Container Ship Loading Optimization
1.33 Pump Combination Optimization
1.34 Medical Resource Allocation Optimization
1.35 Visual Base Station and Drone Site Selection Optimization
**2 Machine Learning and Deep Learning Classification and Prediction**
**2.1 Machine Learning and Deep Learning Classification**
2.1.1 BiLSTM Bidirectional Long Short-Term Memory Neural Network Classification
2.1.2 BP Neural Network Classification
2.1.3 CNN Convolutional Neural Network Classification
2.1.4 DBN Deep Belief Network Classification
2.1.5 DELM Deep Learning Extreme Learning Machine Classification
2.1.6 ELMAN Recurrent Neural Network Classification
2.1.7 ELM Extreme Learning Machine Classification
2.1.8 GRNN General Regression Neural Network Classification
2.1.9 GRU Gated Recurrent Unit Classification
2.1.10 KELM Hybrid Kernel Extreme Learning Machine Classification
2.1.11 KNN Classification
2.1.12 LSSVM Least Squares Support Vector Machine Classification
2.1.13 LSTM Long Short-Term Memory Network Classification
2.1.14 MLP Multi-Layer Perceptron Classification
2.1.15 PNN Probabilistic Neural Network Classification
2.1.16 RELM Robust Extreme Learning Machine Classification
2.1.17 RF Random Forest Classification
2.1.18 SCN Stochastic Configuration Network Model Classification
2.1.19 SVM Support Vector Machine Classification
2.1.20 XGBOOST Classification
**2.2 Machine Learning and Deep Learning Prediction**
2.2.1 ANFIS Adaptive Neuro-Fuzzy Inference System Prediction
2.2.2 ANN Artificial Neural Network Prediction
2.2.3 ARMA Autoregressive Moving Average Model Prediction
2.2.4 BF Particle Filter Prediction
2.2.5 BiLSTM Bidirectional Long Short-Term Memory Neural Network Prediction
2.2.6 BLS Broad Learning System Prediction
2.2.7 BP Neural Network Prediction
2.2.8 CNN Convolutional Neural Network Prediction
2.2.9 DBN Deep Belief Network Prediction
2.2.10 DELM Deep Learning Extreme Learning Machine Prediction
2.2.11 DKELM Regression Prediction
2.2.12 ELMAN Recurrent Neural Network Prediction
2.2.13 ELM Extreme Learning Machine Prediction
2.2.14 ESN Echo State Network Prediction
2.2.15 FNN Feedforward Neural Network Prediction
2.2.16 GMDN Prediction
2.2.17 GMM Gaussian Mixture Model Prediction
2.2.18 GRNN General Regression Neural Network Prediction
2.2.19 GRU Gated Recurrent Unit Prediction
2.2.20 KELM Hybrid Kernel Extreme Learning Machine Prediction
2.2.21 LMS Least Mean Squares Algorithm Prediction
2.2.22 LSSVM Least Squares Support Vector Machine Prediction
2.2.23 LSTM Long Short-Term Memory Network Prediction
2.2.24 RBF Radial Basis Function Neural Network Prediction
2.2.25 RELM Robust Extreme Learning Machine Prediction
2.2.26 RF Random Forest Prediction
2.2.27 RNN Recurrent Neural Network Prediction
2.2.28 RVM Relevance Vector Machine Prediction
2.2.29 SVM Support Vector Machine Prediction
2.2.30 TCN Temporal Convolutional Network Prediction
2.2.31 XGBoost Regression Prediction
2.2.32 Fuzzy Prediction
2.2.33 Singular Spectrum Analysis Method SSA Time Series Prediction
**2.3 Machine Learning and Deep Learning Practical Application Prediction**
CPI index prediction, PM2.5 concentration prediction, SOC prediction, financial warning prediction, yield prediction, parking space prediction, pest prediction, strip steel thickness prediction, battery health status prediction, power load prediction, housing price prediction, corrosion rate prediction, fault diagnosis prediction, photovoltaic power prediction, trajectory prediction, aircraft engine lifespan prediction, exchange rate prediction, concrete strength prediction, heating furnace temperature prediction, price prediction, traffic flow prediction, consumer price index prediction, air quality prediction, grain temperature prediction, temperature prediction, clear water value prediction, unemployment rate prediction, electricity consumption prediction, transportation volume prediction, manufacturing purchasing manager index prediction
**3 Image Processing Aspects**
**3.1 Image Edge Detection**
**3.2 Image Processing**
**3.3 Image Segmentation**
**3.4 Image Classification**
**3.5 Image Tracking**
**3.6 Image Encryption and Decryption**
**3.7 Image Retrieval**
**3.8 Image Registration**
**3.9 Image Stitching**
**3.10 Image Evaluation**
**3.11 Image Denoising**
**3.12 Image Fusion**
**3.13 Image Recognition**
3.13.1 Dial Recognition
3.13.2 Lane Line Recognition
3.13.3 Vehicle Counting
3.13.4 Vehicle Recognition
3.13.5 License Plate Recognition
3.13.6 Parking Space Recognition
3.13.7 Size Detection
3.13.8 Answer Sheet Recognition
3.13.9 Appliance Recognition
3.13.10 Fall Detection
3.13.11 Animal Recognition
3.13.12 QR Code Recognition
3.13.13 Invoice Recognition
3.13.14 Clothing Recognition
3.13.15 Chinese Character Recognition
3.13.16 Traffic Light Recognition
3.13.17 Iris Recognition
3.13.18 Fire Detection
3.13.19 Disease Classification
3.13.20 Traffic Sign Recognition
3.13.21 Card Number Recognition
3.13.22 Mask Recognition
3.13.23 Crack Detection
3.13.24 Target Tracking
3.13.25 Fatigue Detection
3.13.26 Flag Recognition
3.13.27 Grass Recognition
3.13.28 Face Recognition
3.13.29 Renminbi Recognition
3.13.30 ID Card Recognition
3.13.31 Gesture Recognition
3.13.32 Digit and Letter Recognition
3.13.33 Palm Recognition
3.13.34 Leaf Recognition
3.13.35 Fruit Recognition
3.13.36 Barcode Recognition
3.13.37 Temperature Detection
3.13.38 Defect Detection
3.13.39 Chip Detection
3.13.40 Behavior Recognition
3.13.41 Verification Code Recognition
3.13.42 Medicinal Material Recognition
3.13.43 Coin Recognition
3.13.44 Postal Code Recognition
3.13.45 Playing Card Recognition
3.13.46 Fingerprint Recognition
**3.14 Image Restoration**
**3.15 Image Compression**
**3.16 Image Steganography**
**3.17 Image Enhancement**
**3.18 Image Reconstruction**
**4 Path Planning Aspects**
**4.1 Traveling Salesman Problem (TSP)**
4.1.1 Single Traveling Salesman Problem (TSP)
4.1.2 Multiple Traveling Salesman Problem (MTSP)
**4.2 Vehicle Routing Problem (VRP)**
4.2.1 Vehicle Routing Problem (VRP)
4.2.2 Capacitated Vehicle Routing Problem (CVRP)
4.2.3 Capacitated + Time Window + Distance Vehicle Routing Problem (DCTWVRP)
4.2.4 Capacitated + Distance Vehicle Routing Problem (DCVRP)
4.2.5 Distance Vehicle Routing Problem (DVRP)
4.2.6 Vehicle Routing Problem with Charging Stations + Time Windows (ETWVRP)
4.2.3 Vehicle Routing Problem with Multiple Capacities (MCVRP)
4.2.4 Distance Multi-Vehicle Routing Problem (MDVRP)
4.2.5 Simultaneous Pickup and Delivery Vehicle Routing Problem (SDVRP)
4.2.6 Vehicle Routing Problem with Time Windows + Capacities (TWCVRP)
4.2.6 Vehicle Routing Problem with Time Windows (TWVRP)
**4.3 Multimodal Transport Problem**
**4.4 Robot Path Planning**
4.4.1 Obstacle Avoidance Path Planning
4.4.2 Maze Path Planning
4.4.3 Grid Map Path Planning
**4.5 Delivery Path Planning**
4.5.1 Cold Chain Delivery Path Planning
4.5.2 Takeout Delivery Path Planning
4.5.3 Mask Delivery Path Planning
4.5.4 Medicine Delivery Path Planning
4.5.5 Delivery Path Planning with Charging Stations
4.5.6 Chain Supermarket Delivery Path Planning
4.5.7 Vehicle Collaborative Drone Delivery Path Planning
**4.6 Drone Path Planning**
4.6.1 Aircraft Simulation
4.6.2 Drone Flight Operations
4.6.3 Drone Trajectory Tracking
4.6.4 Drone Swarm Simulation
4.6.5 3D Path Planning for Drones
4.6.6 Drone Formation
4.6.7 Drone Cooperative Tasks
4.6.8 Drone Task Allocation
**5 Speech Processing**
**5.1 Speech Emotion Recognition**
**5.2 Sound Source Localization**
**5.3 Feature Extraction**
**5.4 Speech Coding**
**5.5 Speech Processing**
**5.6 Speech Separation**
**5.7 Speech Analysis**
**5.8 Speech Synthesis**
**5.9 Speech Encryption**
**5.10 Speech Denoising**
**5.11 Speech Recognition**
**5.12 Speech Compression**
**5.13 Speech Hiding**
**6 Cellular Automata Aspects**
**6.1 Cellular Automata Virus Simulation**
**6.2 Cellular Automata Urban Planning**
**6.3 Cellular Automata Traffic Flow**
**6.4 Cellular Automata Gas**
**6.5 Cellular Automata Personnel Evacuation**
**6.6 Cellular Automata Forest Fire**
**6.7 Cellular Automata Game of Life**
**7 Signal Processing Aspects**
**7.1 Fault Signal Diagnosis Analysis**
7.1.1 Gear Damage Identification
7.1.2 Asynchronous Motor Rotor Bar Fault Diagnosis
7.1.3 Rolling Element Inner and Outer Ring Fault Diagnosis Analysis
7.1.4 Motor Fault Diagnosis Analysis
7.1.5 Bearing Fault Diagnosis Analysis
7.1.6 Gearbox Fault Diagnosis Analysis
7.1.7 Three-Phase Inverter Fault Diagnosis Analysis
7.1.8 Diesel Engine Fault Diagnosis
**7.2 Radar Communication**
7.2.1 FMCW Simulation
7.2.2 GPS Anti-jamming
7.2.3 Radar LFM
7.2.4 Radar MIMO
7.2.5 Radar Angle Measurement
7.2.6 Radar Imaging
7.2.7 Radar Positioning
7.2.8 Radar Echo
7.2.9 Radar Detection
7.2.10 Radar Digital Signal Processing
7.2.11 Radar Communication
7.2.12 Radar Phased Array
7.2.13 Radar Signal Analysis
7.2.14 Radar Early Warning
7.2.15 Radar Pulse Compression
7.2.16 Antenna Pattern
7.2.17 Radar Clutter Simulation
**7.3 Biological Electrical Signals**
7.3.1 Electromyography (EMG)
7.3.2 Electroencephalography (EEG)
7.3.3 Electrocardiography (ECG)
7.3.4 Heart Simulation
**7.4 Communication Systems**
7.4.1 DOA Estimation
7.4.2 LEACH Protocol
7.4.3 Encoding and Decoding
7.4.4 Variational Mode Decomposition
7.4.5 Ultra-Wideband Simulation
7.4.6 Multipath Fading Simulation
7.4.7 Cellular Networks
7.4.8 Pipeline Leakage
7.4.9 Empirical Mode Decomposition
7.4.10 Filter Design
7.4.11 Analog Signal Transmission
7.4.12 Analog Signal Modulation
7.4.13 Digital Baseband Signals
7.4.14 Digital Channels
7.4.15 Digital Signal Processing
7.4.16 Digital Signal Transmission
7.4.17 Digital Signal Denoising
7.4.18 Underwater Acoustic Communication
7.4.19 Communication Simulation
7.4.20 Wireless Transmission
7.4.21 Bit Error Rate Simulation
7.4.22 Modern Communication
7.4.23 Channel Estimation
7.4.24 Signal Detection
7.4.25 Signal Fusion
7.4.26 Signal Recognition
7.4.27 Compressed Sensing
7.4.28 Noise Simulation
7.4.29 Noise Interference
**7.5 Drone Communication**
**7.6 Wireless Sensor Localization and Layout**
7.6.1 WSN Localization
7.6.2 Height Estimation
7.6.3 Filter Tracking
7.6.4 Target Localization
7.6.4.1 Dv-Hop Localization
7.6.4.2 RSSI Localization
7.6.4.3 Intelligent Algorithm Optimization Localization
7.6.5 Integrated Navigation
**8 Power System Aspects**
Microgrid Optimization, Reactive Power Optimization, Distribution Network Reconstruction, Energy Storage Configuration
