1 简介
1.1 灰狼算法介绍
2 部分代码
%-------------------------------------------------------------------%% Binary Grey Wolf Optimization (BGWO) demo version %%-------------------------------------------------------------------%%---Input------------------------------------------------------------% feat : feature vector (instances x features)% label : label vector (instances x 1)% N : Number of wolves% max_Iter : Maximum number of iterations%---Output-----------------------------------------------------------% sFeat : Selected features (instances x features)% Sf : Selected feature index% Nf : Number of selected features% curve : Convergence curve%--------------------------------------------------------------------%% Binary Grey Wolf Optimization (Version 1)clc, clear, close% Benchmark data setload ionosphere.mat;% Set 20% data as validation setho = 0.2;% Hold-out methodHO = cvpartition(label,'HoldOut',ho,'Stratify',false);% Parameter settingN = 10;max_Iter = 100;% Binary Grey Wolf Optimization[sFeat,Sf,Nf,curve] = jBGWO1(feat,label,N,max_Iter,HO);% Plot convergence curveplot(1:max_Iter,curve);xlabel('Number of Iterations');ylabel('Fitness Value');title('BGWO1'); grid on;%% Binary Grey Wolf Optimization (Version 2)clc, clear, close;% Benchmark data setload ionosphere.mat;% Set 20% data as validation setho = 0.2;% Hold-out methodHO = cvpartition(label,'HoldOut',ho,'Stratify',false);% Parameter settingN = 10;max_Iter = 100;% Binary Grey Wolf Optimization[sFeat,Sf,Nf,curve] = jBGWO2(feat,label,N,max_Iter,HO);% Plot convergence curveplot(1:max_Iter,curve);xlabel('Number of Iterations');ylabel('Fitness Value');title('BGWO2'); grid on;
3 仿真结果
4 参考文献
[1]江丹丹. 基于改进的多目标灰狼优化算法的碳交易价格预测[D]. 兰州大学.
博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，相关matlab代码问题可私信交流。
部分理论引用网络文献，若有侵权联系博主删除。
