1 简介
This paper studies the constrained optimization problem for nonlinear diesel blending. A new hybrid algorithm called cultural harmony search algorithm is presented to solve the proposed optimization problem, which uses cultural knowledge in the belief space of the cultural algorithm to guide the evolving and searching process of the harmony search algorithm. Then, an improved harmony improvisation in the population space of cultural algorithm is developed for new harmony generation to enrich the population diversity. Moreover, in order to accelerate convergence, the domain of decision variables is scaled down by a simplex method at the beginning of the algorithm, and a simplex improved cultural harmony search algorithm is provided. Finally, benchmark functions and the results of application in nonlinear diesel blending of a real-world refinery show the feasibility and effectiveness of the proposed algorithms. The contrasted experiments show that our proposed hybrid algorithm is better than other hybrid algorithms, especially in diesel blending optimization problem.
2 部分代码
%% Cultural Harmony Learning Algorithm - Created in 18 Jan 2022 by Seyed Muhammad Hossein Mousavi
% Here is all about learning with evolutionary algorithms. Harmony search
% and cultural algorithms are two fast optimization algorithms which their
% result are combined here in order to train inputs for targets in a simple
% dataset. Basically, system starts with making initial fuzzy model and fit
% the outputs based on inputs by harmony search first and then tries to fit the
% harmony search outputs with inputs in the second stage. That means we are using both
% evolutionary algorithms to improve the accuracy. System easily could be
% used for regression, classification and other optimization tasks. You can
%% Cleaning
clc;
clear;
warning('off');
%% Data Loading
data=JustLoad();
%% Generate Basic Fuzzy Model
% Number of Clusters in FCM
ClusNum=4;
%
fis=GenerateFuzzy(data,ClusNum);
%
%% Tarining Cultural Harmony Algorithm
% Harmony Search Learning
HarFis=hars(fis,data);
% Harmony Cultural Algorithm Learning
CAHSfis=CulturalFCN(HarFis,data);
%% Plot Cultural Harmony Results (Train - Test)
% Train Output Extraction
TrTar=data.TrainTargets;
TrainOutputs=evalfis(data.TrainInputs,CAHSfis);
% Test Output Extraction
TsTar=data.TestTargets;
TestOutputs=evalfis(data.TestInputs,CAHSfis);
% Train calc
Errors=data.TrainTargets-TrainOutputs;
MSE=mean(Errors.^2);RMSE=sqrt(MSE);
error_mean=mean(Errors);error_std=std(Errors);
% Test calc
Errors1=data.TestTargets-TestOutputs;
MSE1=mean(Errors1.^2);RMSE1=sqrt(MSE1);
error_mean1=mean(Errors1);error_std1=std(Errors1);
% Train
figure('units','normalized','outerposition',[0 0 1 1])
subplot(3,2,1);
plot(data.TrainTargets,'c');
hold on;
plot(TrainOutputs,'k');legend('Target','Output');
title('Cultural Harmony Training Part');xlabel('Sample Index');grid on;
% Test
subplot(3,2,2);
plot(data.TestTargets,'c');
hold on;
plot(TestOutputs,'k');legend('Cultural Harmony Target','Cultural Harmony Output');
title('Cultural Harmony Testing Part');xlabel('Sample Index');grid on;
% Train
subplot(3,2,3);
plot(Errors,'k');legend('Cultural Harmony Training Error');
title(['Train MSE = ' num2str(MSE) ' , Train RMSE = ' num2str(RMSE)]);grid on;
% Test
subplot(3,2,4);
plot(Errors1,'k');legend('Cultural Harmony Testing Error');
title(['Test MSE = ' num2str(MSE1) ' , Test RMSE = ' num2str(RMSE1)]);grid on;
% Train
subplot(3,2,5);
h=histfit(Errors, 50);h(1).FaceColor = [.1 .2 0.9];
title(['Train Error Mean = ' num2str(error_mean) ' , Train Error STD = ' num2str(error_std)]);
% Test
subplot(3,2,6);
h=histfit(Errors1, 50);h(1).FaceColor = [.1 .2 0.9];
title(['Test Error Mean = ' num2str(error_mean1) ' , Test Error STD = ' num2str(error_std1)]);
%% Plot Just Fuzzy Results (Train - Test)
% Train Output Extraction
fTrainOutputs=evalfis(data.TrainInputs,fis);
% Test Output Extraction
fTestOutputs=evalfis(data.TestInputs,fis);
% Train calc
fErrors=data.TrainTargets-fTrainOutputs;
fMSE=mean(fErrors.^2);fRMSE=sqrt(fMSE);
ferror_mean=mean(fErrors);ferror_std=std(fErrors);
% Test calc
fErrors1=data.TestTargets-fTestOutputs;
fMSE1=mean(fErrors1.^2);fRMSE1=sqrt(fMSE1);
ferror_mean1=mean(fErrors1);ferror_std1=std(fErrors1);
% Train
figure('units','normalized','outerposition',[0 0 1 1])
subplot(3,2,1);
plot(data.TrainTargets,'m');hold on;
plot(fTrainOutputs,'k');legend('Target','Output');
title('Fuzzy Training Part');xlabel('Sample Index');grid on;
% Test
subplot(3,2,2);
plot(data.TestTargets,'m');hold on;
plot(fTestOutputs,'k');legend('Target','Output');
title('Fuzzy Testing Part');xlabel('Sample Index');grid on;
% Train
subplot(3,2,3);
plot(fErrors,'g');legend('Fuzzy Training Error');
title(['Train MSE = ' num2str(fMSE) ' , Test RMSE = ' num2str(fRMSE)]);grid on;
% Test
subplot(3,2,4);
plot(fErrors1,'g');legend('Fuzzy Testing Error');
title(['Train MSE = ' num2str(fMSE1) ' , Test RMSE = ' num2str(fRMSE1)]);grid on;
% Train
subplot(3,2,5);
h=histfit(fErrors, 50);h(1).FaceColor = [.3 .8 0.3];
title(['Train Error Mean = ' num2str(ferror_mean) ' , Train Error STD = ' num2str(ferror_std)]);
% Test
subplot(3,2,6);
h=histfit(fErrors1, 50);h(1).FaceColor = [.3 .8 0.3];
title(['Test Error Mean = ' num2str(ferror_mean1) ' , Test Error STD = ' num2str(ferror_std1)]);3 仿真结果
4 参考文献
[1] Gao M , Zhu Y , Cao C , et al. A Hybrid Cultural Harmony Search Algorithm for Constrained Optimization Problem of Diesel Blending[J]. IEEE Access, 2019, PP(99):1-1.
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