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πAuthor Introduction: Graduate student from a “985” university, researcher in the field of Matlab.
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β Research Scope: Complete Code, Paper Reproduction, Program Customization, Journal Writing, Research Collaboration
πCode Acquisition Method: How to Obtain Matlab Code
For more Matlab optimization simulation content, click belowπ
Matlab Optimization (Advanced Version)
Introduction
πππThe table of contents is as follows: πππ
Contents
π₯1 Overview
π2 Code and Running Steps
π3 Running Results
π4 Matlab Version and References
π5 Code Acquisition Method
π 6 Simulation Consultation
1 Overview
The Artificial Bee Colony Algorithm (ABC) solves the routing optimization problem in wireless sensor networks (WSN) (including network lifetime and efficiency comparison).
The Artificial Bee Colony Algorithm (ABC) is an optimization algorithm based on the foraging behavior of bees, used to solve optimization problems. In wireless sensor networks (WSN), the ABC algorithm can be applied to routing optimization problems to extend the network’s lifetime and improve efficiency.
Principle:
The ABC algorithm simulates the behavior of bees during foraging, including the search and discovery of food sources, as well as the sharing and updating of information. During the search process, bees evaluate the quality of flowers based on the scent of pollen and select the best flowers for harvesting. In the ABC algorithm, bees represent potential solutions, while the food source represents the value of the optimization objective, and bees select and update based on the quality of the food sources.
Process:
1 Initialization: Randomly generate an initial bee population, with each bee representing a solution.
2 Search Phase:
– Worker Bee Phase: Bees search the surrounding solution space based on their current position and select better food sources according to the evaluation function.
– Observer Bee Phase: Some bees observe the information of other bees and jump to their positions to continue searching.
3 Update Phase:
– Scout Bee Phase: Some bees randomly explore unsearched areas to prevent the algorithm from falling into local optima.
– Update Food Source Phase: Update the food sources based on the information collected by bees during the search, retaining the best food sources.
4 Termination Condition: When certain termination conditions (such as reaching the iteration count) are met, the algorithm ends and outputs the optimal solution.
Routing Optimization Problem in Wireless Sensor Networks:
In WSNs, the routing optimization problem aims to find the best data transmission path to maximize the network’s lifetime, improve transmission efficiency, and reduce energy consumption.
Comparison of Network Lifetime and Efficiency Before and After:
After using the ABC algorithm for routing optimization, a better data transmission path can be obtained, thereby extending the network’s lifetime and improving network efficiency. The network optimized by the ABC algorithm performs better in energy consumption balance and data transmission delay, making the entire network more stable and reliable.
2 Code and Running Steps
(1) Directly run main.m to generate the graph with one click.
3Running Results
3Running Results
4Matlab Version and References
1 Matlab Version
2019b
2 References
[1] Liao Li, Wang Huadong. Energy Balance Algorithm for Wireless Sensor Networks Based on Artificial Bee Colony [J]. Computer Measurement and Control. 2015
3 Notes
This section is excerpted from the internet for reference only. If there is any infringement, please contact for deletion.
5 Code Acquisition Method
6 Simulation Consultation
**π Simulation Consultation
1 Various Intelligent Optimization Algorithms Improvement and Application
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 Allocation 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 Absorbing Wave 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 Schedule Optimization
1.32 Container Ship Loading Optimization
1.33 Pump Combination Optimization
1.34 Medical Resource Allocation Optimization
1.35 Visual Domain Base Station and Drone Location 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 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 Network Prediction
2.2.6 BLS Width Learning Neural Network 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 for Time Series Prediction
**2.3 Practical Applications of Machine Learning and Deep Learning Predictions**
CPI Index Prediction, PM2.5 Concentration Prediction, SOC Prediction, Financial Warning Prediction, Yield Prediction, Parking Space Prediction, Pest Prediction, 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 Life Prediction, Exchange Rate Prediction, Concrete Strength Prediction, Heating Furnace Temperature Prediction, Price Prediction, Traffic Flow Prediction, Resident Consumption Index Prediction, Air Quality Prediction, Grain Temperature Prediction, Temperature Prediction, Clear Water Value Prediction, Unemployment Rate Prediction, Electricity Consumption Prediction, Transport Volume Prediction, Manufacturing Purchasing Managers’ Index Prediction
**3 Image Processing**
**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 Object Tracking
3.13.25 Fatigue Detection
3.13.26 Flag Recognition
3.13.27 Grass Recognition
3.13.28 Face Recognition
3.13.29 RMB 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 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**
**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 Capacity-Constrained Vehicle Routing Problem (CVRP)
4.2.3 Capacity + Time Window + Distance Vehicle Routing Problem (DCTWVRP)
4.2.4 Capacity + Distance Vehicle Routing Problem (DCVRP)
4.2.5 Distance-Constrained Vehicle Routing Problem (DVRP)
4.2.6 Charging Station + Time Window Vehicle Routing Problem (ETWVRP)
4.2.3 Multi-Capacity Vehicle Routing Problem (MCVRP)
4.2.4 Distance-Constrained Multi-Vehicle Routing Problem (MDVRP)
4.2.5 Simultaneous Pickup and Delivery Vehicle Routing Problem (SDVRP)
4.2.6 Time Window + Capacity Vehicle Routing Problem (TWCVRP)
4.2.6 Time Window Vehicle Routing Problem (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 Takeaway 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 Station
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 Drone 3D Path Planning
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**
**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 Crowd Evacuation**
**6.6 Cellular Automata Forest Fire**
**6.7 Cellular Automata Game of Life**
**7 Signal Processing**
**7.1 Fault Signal Diagnosis Analysis**
7.1.1 Gear Damage Recognition
7.1.2 Asynchronous Motor Rotor Broken 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-Interference
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 Localization
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 Radiation Pattern
7.2.17 Radar Clutter Simulation
**7.3 Biological Electrical Signals**
7.3.1 Electromyography Signal (EMG)
7.3.2 Electroencephalography Signal (EEG)
7.3.3 Electrocardiogram Signal (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 Signal
7.4.14 Digital Channel
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 Positioning and Layout**
7.6.1 WSN Positioning
7.6.2 Height Estimation
7.6.3 Filtering and 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 Combined Navigation
**8 Power System**
Microgrid Optimization, Reactive Power Optimization, Distribution Network Reconstruction, Energy Storage Configuration