🍁🥬🕒摘要🕒🥬🍁
萤火虫算法模拟了萤火虫的自然现象。真实的萤火虫自然地呈现出一种离散的闪烁模式，而萤火虫算法假设它们总是在发光。为了模拟萤火虫的这种闪烁行为，Yang Xin-She提出了了三条规则（Yang，2009）：
（1）假设所有萤火虫都是雌雄同体的，因此一只萤火虫可能会被其他任何萤火虫吸引。
（2）萤火虫的亮度决定其吸引力的大小，较亮的萤火虫吸引较暗的萤火虫。如果没有萤火虫比被考虑的萤火虫更亮，它就会随机移动。
（3）函数的最优值与萤火虫的亮度成正比。
✨🔎⚡运行结果⚡🔎✨
💂♨️👨🎓Matlab代码👨🎓♨️💂
clc; clear; warning('off'); % Data Loading data=JustLoad(); % Generate Fuzzy Model ClusNum=4; % Number of Clusters in FCM % fis=GenerateFuzzy(data,ClusNum); % %% Tarining FireFly Algorithm FireFlyFis=FireFlyRegression(fis,data); %% Plot Fuzzy FireFly Results (Train - Test) % Train Output Extraction TrTar=data.TrainTargets; TrainOutputs=evalfis(data.TrainInputs,FireFlyFis); % Test Output Extraction TsTar=data.TestTargets; TestOutputs=evalfis(data.TestInputs,FireFlyFis); % 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('FireFly Training Part');xlabel('Sample Index');grid on; % Test subplot(3,2,2); plot(data.TestTargets,'c');hold on; plot(TestOutputs,'k');legend('FireFly Target','FireFly Output'); title('FireFly Testing Part');xlabel('Sample Index');grid on; % Train subplot(3,2,3); plot(Errors,'k');legend('FireFly Training Error'); title(['Train MSE = ' num2str(MSE) ' , Train RMSE = ' num2str(RMSE)]);grid on; % Test subplot(3,2,4); plot(Errors1,'k');legend('FireFly 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 = [.8 .8 0.3]; 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 = [.8 .8 0.3]; 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)]); %% Regression Plots figure('units','normalized','outerposition',[0 0 1 1]) subplot(2,2,1) [population2,gof] = fit(TrTar,TrainOutputs,'poly4'); plot(TrTar,TrainOutputs,'o',... 'LineWidth',1,... 'MarkerSize',6,... 'MarkerEdgeColor','g',... 'MarkerFaceColor',[0.9,0.1,0.1]); title(['FireFly Train - R = ' num2str(1-gof.rmse)]); xlabel('Train Target'); ylabel('Train Output'); hold on plot(population2,'b-','predobs'); xlabel('Train Target'); ylabel('Train Output'); hold off subplot(2,2,2) [population2,gof] = fit(TsTar, TestOutputs,'poly4'); plot(TsTar, TestOutputs,'o',... 'LineWidth',1,... 'MarkerSize',6,... 'MarkerEdgeColor','g',... 'MarkerFaceColor',[0.9,0.1,0.1]); title(['FireFly Test - R = ' num2str(1-gof.rmse)]); xlabel('Test Target'); ylabel('Test Output'); hold on plot(population2,'b-','predobs'); xlabel('Test Target'); ylabel('Test Output'); hold off subplot(2,2,3) [population2,gof] = fit(TrTar,fTrainOutputs,'poly4'); plot(TrTar,fTrainOutputs,'o',... 'LineWidth',1,... 'MarkerSize',6,... 'MarkerEdgeColor','b',... 'MarkerFaceColor',[0.3,0.9,0.2]); title(['Fuzzy Train - R = ' num2str(1-gof.rmse)]); xlabel('Train Target'); ylabel('Train Output'); hold on plot(population2,'r-','predobs'); xlabel('Train Target'); ylabel('Train Output'); hold off subplot(2,2,4) [population2,gof] = fit(TsTar, fTestOutputs,'poly4'); plot(TsTar, fTestOutputs,'o',... 'LineWidth',1,... 'MarkerSize',6,... 'MarkerEdgeColor','b',... 'MarkerFaceColor',[0.3,0.9,0.2]); title(['Fuzzy Test - R = ' num2str(1-gof.rmse)]); xlabel('Test Target'); ylabel('Test Output'); hold on plot(population2,'r-','predobs'); xlabel('Test Target'); ylabel('Test Output'); hold off
📜📢🌈参考文献🌈📢📜
[1]王昕, 黄柯, 郑益慧,等. 基于萤火虫算法-广义回归神经网络的光伏发电功率组合预测[J]. 电网技术, 2017, 41(2):7.
