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Intelligent optimization algorithm Neural network prediction Radar communication Wireless sensor Power system
Signal processing Image processing Path planning Cellular automaton Drone
Content introduction
With the development of technology and the continuous accumulation of data, data mining and machine learning have become one of the hottest fields today. Among them, data regression prediction is one of the most basic problems in machine learning, and its application range is wide, including finance, medical, industry and other fields.
In recent years, XGBoost has become one of the most popular algorithms in data regression prediction. XGBoost is an ensemble learning algorithm based on decision trees, which has the advantages of high efficiency, accuracy, and strong interpretability. However, the XGBoost algorithm also has some problems, such as being prone to overfitting. Therefore, how to optimize the XGBoost algorithm and improve its accuracy has become the focus of researchers.
In this article, we will introduce a method to optimize XGBoost based on the tuna algorithm TSO to achieve data regression prediction. This method can effectively improve the accuracy of the XGBoost algorithm and avoid problems such as over-fitting. Below, we will introduce the specific implementation process of this method in detail.
First, we need to understand the tuna algorithm TSO. The tuna algorithm is an optimization algorithm based on swarm intelligence and has the advantages of global search capability and fast convergence speed. TSO is an improved algorithm of the tuna algorithm that can better solve optimization problems. Therefore, we can use the TSO algorithm to optimize the XGBoost algorithm.
The specific implementation process is as follows:
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First, we need to prepare the data set. We can choose some public data sets for experiments, such as Boston Housing, Iris and other data sets.
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Then, we need to preprocess the data. Preprocessing includes data cleaning, feature selection, feature scaling and other steps.
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Then, we can start using the XGBoost algorithm for data regression prediction. Before using the XGBoost algorithm, we need to adjust its parameters. Parameter adjustment can help us find the optimal combination of hyperparameters, thereby improving the accuracy of the algorithm. Commonly used parameter adjustment methods include grid search, random search, etc.
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After the XGBoost algorithm parameter adjustment is completed, we can use the TSO algorithm to optimize it. The specific implementation process of the TSO algorithm is as follows:
(1) Initialize the tuna group, including the position, speed and other information of the tuna.
(2) Calculate the fitness value of each tuna, that is, the accuracy of the XGBoost algorithm under the current hyperparameter combination.
(3) Sort the tuna according to the fitness value, and select the tuna with higher fitness value as the elite of the population.
(4) Update the position and speed information of the entire tuna group based on the elite’s position information.
(5) Repeat steps 2-4 until the preset number of iterations is reached or the optimal solution is reached.
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Finally, we can use the optimized XGBoost algorithm to make predictions on the test set and evaluate the accuracy of the algorithm. Commonly used evaluation indicators include mean square error, mean absolute error, etc.
Through the above steps, we can effectively optimize the XGBoost algorithm, improve its accuracy, and avoid problems such as overfitting. This method has broad application prospects in practical applications and can provide effective solutions to data regression prediction problems in various fields.
Part of the code
%% Clear environment variables</code><code>warning off % Close alarm information</code><code>close all % Close open figure window</code><code>clear % Clear variables</code><code>clc % clear command line</code><code>?</code><code>%% import data</code><code>res = xlsread('dataset.xlsx');</code><code>?</code><code>%% divide the training set and test set</code><code>temp = randperm(357);</code><code>?</code><code>P_train = res(temp(1: 240), 1: 12)';</code><code>T_train = res(temp(1: 240), 13)';</code><code>M = size(P_train, 2);</code><code>?</code><code>P_test = res(temp(241: end), 1: 12)';</code><code>T_test = res(temp(241: end), 13)';</code><code>N = size(P_test, 2);</code><code>?</code><code>%% data normalization化</code><code>[p_train, ps_input] = mapminmax(P_train, 0, 1);</code><code>p_test = mapminmax('apply', P_test, ps_input);</code><code>t_train = ind2vec(T_train);</code><code>t_test = ind2vec(T_test );
Operation results
References
[1] Zhang Yu, Li Minjie, Chen Huimin, et al. QSPR method and system for constructing an interpretable XGBoost regression model to predict PCE based on SHAP values: CN202111001675.4[P].CN113808680A[2023-10-10].
[2] Wang Kunzhang, Jiang Shubo, Zhang Hao, et al. Regression-classification-regression life prediction model based on XGBoost[J].[2023-10-10].
[3] Hu Er. Forecasting the supply and demand gap of Didi Chuxing based on xgboost regression algorithm [D]. Southwestern University of Finance and Economics, 2017.
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1 Improvements and applications of various intelligent optimization algorithms
Production scheduling, economic scheduling, assembly line scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, three-dimensional packing, logistics site selection, cargo space optimization, bus scheduling optimization, charging pile layout optimization, workshop layout optimization, Container ship stowage optimization, water pump combination optimization, medical resource allocation optimization, facility layout optimization, visible area base station and drone site selection optimization
2 Machine learning and deep learning
Convolutional neural network (CNN), LSTM, support vector machine (SVM), least squares support vector machine (LSSVM), extreme learning machine (ELM), kernel extreme learning machine (KELM), BP, RBF, width Learning, DBN, RF, RBF, DELM, XGBOOST, TCN realize wind power prediction, photovoltaic prediction, battery life prediction, radiation source identification, traffic flow prediction, load prediction, stock price prediction, PM2.5 concentration prediction, battery health status prediction, water body Optical parameter inversion, NLOS signal identification, accurate subway parking prediction, transformer fault diagnosis
2. Image processing
Image recognition, image segmentation, image detection, image hiding, image registration, image splicing, image fusion, image enhancement, image compressed sensing
3 Path planning
Traveling salesman problem (TSP), vehicle routing problem (VRP, MVRP, CVRP, VRPTW, etc.), UAV three-dimensional path planning, UAV collaboration, UAV formation, robot path planning, raster map path planning , multimodal transportation problems, vehicle collaborative UAV path planning, antenna linear array distribution optimization, workshop layout optimization
4 UAV applications
UAV path planning, UAV control, UAV formation, UAV collaboration, UAV task allocation, and online optimization of UAV safe communication trajectories
5 Wireless sensor positioning and layout
Sensor deployment optimization, communication protocol optimization, routing optimization, target positioning optimization, Dv-Hop positioning optimization, Leach protocol optimization, WSN coverage optimization, multicast optimization, RSSI positioning optimization
6 Signal processing
Signal recognition, signal encryption, signal denoising, signal enhancement, radar signal processing, signal watermark embedding and extraction, EMG signal, EEG signal, signal timing optimization
7 Power system aspects
Microgrid optimization, reactive power optimization, distribution network reconstruction, energy storage configuration
8 Cellular Automata
Traffic flow, crowd evacuation, virus spread, crystal growth
9 Radar aspect
Kalman filter tracking, track correlation, track fusion
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