GWO-BP regression prediction | Matlab gray wolf optimization algorithm optimizes BP neural network regression prediction

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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

In today’s information age, data prediction has become an integral part of many fields. From predictions of financial markets to weather forecasts, data predictions can help us make smarter decisions. As a commonly used data prediction model, BP neural network has achieved success in many fields.

However, BP neural network also has some problems, such as easy to fall into the local optimal solution and slow convergence speed. In order to solve these problems, we can use the gray wolf algorithm to optimize the BP neural network.

The gray wolf algorithm is an optimization algorithm based on the behavior of gray wolf groups in nature. It simulates the behavior of gray wolves in finding the best prey during hunting. By simulating the social behavior and hunting strategies of gray wolves, the gray wolf algorithm can help us find the optimal solution of the BP neural network.

Before using the gray wolf algorithm to optimize the BP neural network, we first need to build a basic BP neural network model. BP neural network is a feedforward neural network with a backpropagation algorithm, which can minimize the error between the predicted output and the actual output by adjusting weights and biases.

Once we have built the BP neural network model, we can start to optimize it using the gray wolf algorithm. First, we need to initialize a group of gray wolves and assign each gray wolf a position and a fitness value. The fitness value can be obtained by calculating the error of the BP neural network, that is, the difference between the predicted output and the actual output.

Then, we need to determine the search range of each gray wolf based on its fitness value. The higher the fitness value of the gray wolf, the larger its search range. This ensures that the gray wolf is more likely to find the optimal solution during the search process.

Next, we needed to model the social behavior and prey-finding strategies of gray wolves. The gray wolf will adjust its search direction and speed based on its location and fitness value. Through continuous iteration, the gray wolves can gradually approach the optimal solution and finally find the optimal weights and biases of the BP neural network.

During the optimization process, we can also introduce some parameters to control the search behavior of the gray wolf algorithm, such as the number of iterations, the number of gray wolf groups, etc. The selection of these parameters will directly affect the quality and efficiency of the optimization results.

By optimizing the BP neural network with the gray wolf algorithm, we can improve the accuracy and efficiency of data prediction. The gray wolf algorithm’s social behavior and prey-finding strategy can help us avoid falling into local optimal solutions, speed up convergence, and find the global optimal solution.

However, it should be noted that the gray wolf algorithm is not a universal optimization algorithm suitable for all problems. Before using the gray wolf algorithm to optimize the BP neural network, we need to conduct reasonable modeling and analysis of the problem to ensure the applicability of the gray wolf algorithm.

In short, optimizing the BP neural network based on the gray wolf algorithm can help us achieve more accurate and efficient data prediction. By simulating the social behavior and hunting strategies of gray wolves, the gray wolf algorithm can help us find the optimal solution of the BP neural network. However, before using the gray wolf algorithm to optimize the BP neural network, we need to conduct reasonable modeling and analysis of the problem to ensure the applicability of the algorithm. Only in the right problem domain can the gray wolf algorithm exert its advantages and bring us better data prediction results.

Part of the code

%_______________________________________________________________%
% Gray Wolf Optimizer (GWO) source codes version 1.0 %
% %
% Developed in MATLAB R2011b(7.13) %
% %
% Author and programmer: Seyedali Mirjalili %
% %
% e-Mail: [email protected] %
% [email protected] %
% %
% Homepage: http://www.alimirjalili.com %
% %
% Main paper: S. Mirjalili, S. M. Mirjalili, A. Lewis %
% Gray Wolf Optimizer, Advances in Engineering %
% Software, in press, %
% DOI: 10.1016/j.advengsoft.2013.12.007 %
% %
%____________________________________________________________%

% This function initializes the first population of search agents
function Positions=initialization(SearchAgents_no,dim,ub,lb)

Boundary_no= size(ub,2); % number of boundaries

% If the boundaries of all variables are equal and user enter a signle
% number for both ub and lb
if Boundary_no==1
    Positions=rand(SearchAgents_no,dim).*(ub-lb) + lb;
end

% If each variable has a different lb and ub
if Boundary_no>1
    for i=1:dim
        ub_i=ub(i);
        lb_i=lb(i);
        Positions(:,i)=rand(SearchAgents_no,1).*(ub_i-lb_i) + lb_i;
    end
end

Running results

GWO-BP regression prediction | Matlab gray wolf optimization algorithm optimizes BP neural network regression prediction _Drone

GWO-BP regression prediction | Matlab gray wolf optimization algorithm optimizes BP neural network regression prediction _Neural Network_02

GWO-BP regression prediction | Matlab gray wolf optimization algorithm optimizes BP neural network regression prediction _Drone_03

References

[1] Yang Yang, Zhao Qing, Qi Lan, et al. Box culvert settlement prediction based on improved GWO-BP neural network model [J]. People’s Yellow River, 2021, 43(10):4.DOI:10.3969/j.issn. 1000-1379.2021.10.029.

[2] Yu Shuxiang, Wen Yijun. Software defect prediction model based on GWO-BP algorithm [J]. Journal of Anhui Electronic Information Vocational and Technical College, 2018, 17(6):5.DOI:CNKI:SUN:AHDJ. 0.2018-06-003.

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1 Improvement and application of various intelligent optimization algorithms
Production scheduling, economic scheduling, assembly line scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, three-dimensional packing, logistics location 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 application
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
Kalman filter tracking, track correlation, track fusion