【特征选择】基于灰狼算法实现二进制特征选择问题附matlab代码

【特征选择】基于灰狼算法实现二进制特征选择问题附matlab代码

​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 set load 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 set load 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.

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