1200字范文 > 回归预测 | MATLAB实现PSO-RF粒子群算法优化随机森林多输入单输出回归预测

回归预测 | MATLAB实现PSO-RF粒子群算法优化随机森林多输入单输出回归预测

时间：2023-02-22 17:11:21

效果一览

基本介绍

MATLAB实现PSO-RF粒子群算法优化随机森林多输入单输出回归预测
粒子群算法优化随机森林(PSO-RF)回归预测（Matlab完整程序和数据）
输入6个特征，输出1个，即多输入单输出；
运行环境Matlab及以上,运行主程序main即可，其余为函数文件无需运行,所有程序放在一个文件夹,data为数据集；
命令窗口输出RMSEP、MAE、R2、MAPE。

程序设计

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%% 定义粒子群算法参数% N 种群 T 迭代次数 %% 随机初始化种群D=dim; %粒子维数c1=1.5; %学习因子1c2=1.5; %学习因子2w=0.8; %惯性权重Xmax=ub;%位置最大值Xmin=lb;%位置最小值Vmax=ub;%速度最大值Vmin=lb;%速度最小值%%%%%%%%%%%%%%%%%%初始化种群个体（限定位置和速度）%%%%%%%%%%%%%%%%x=rand(N,D).*(Xmax-Xmin)+Xmin;v=rand(N,D).*(Vmax-Vmin)+Vmin;%%%%%%%%%%%%%%%%%%初始化个体最优位置和最优值%%%%%%%%%%%%%%%%%%%p=x;pbest=ones(N,1);for i=1:Npbest(i)=fobj(x(i,:)); end%%%%%%%%%%%%%%%%%%%初始化全局最优位置和最优值%%%%%%%%%%%%%%%%%%g=ones(1,D);gbest=inf;for i=1:Nif(pbest(i)<gbest)g=p(i,:);gbest=pbest(i);endend%%%%%%%%%%%按照公式依次迭代直到满足精度或者迭代次数%%%%%%%%%%%%%for i=1:Tifor j=1:N%%%%%%%%%%%%%%更新个体最优位置和最优值%%%%%%%%%%%%%%%%%if (fobj(x(j,:))) <pbest(j)p(j,:)=x(j,:);pbest(j)=fobj(x(j,:)); end%%%%%%%%%%%%%%%%更新全局最优位置和最优值%%%%%%%%%%%%%%%if(pbest(j)<gbest)g=p(j,:);gbest=pbest(j);end%%%%%%%%%%%%%%%%%跟新位置和速度值%%%%%%%%%%%%%%%%%%%%%v(j,:)=w*v(j,:)+c1*rand*(p(j,:)-x(j,:))...+c2*rand*(g-x(j,:));x(j,:)=x(j,:)+v(j,:);%%%%%%%%%%%%%%%%%%%%边界条件处理%%%%%%%%%%%%%%%%%%%%%%if length(Vmax)==1for ii=1:Dif (v(j,ii)>Vmax) | (v(j,ii)< Vmin)v(j,ii)=rand * (Vmax-Vmin)+Vmin;endif (x(j,ii)>Xmax) | (x(j,ii)< Xmin)x(j,ii)=rand * (Xmax-Xmin)+Xmin;endend elsefor ii=1:Dif (v(j,ii)>Vmax(ii)) | (v(j,ii)< Vmin(ii))v(j,ii)=rand * (Vmax(ii)-Vmin(ii))+Vmin(ii);endif (x(j,ii)>Xmax(ii)) | (x(j,ii)< Xmin(ii))x(j,ii)=rand * (Xmax(ii)-Xmin(ii))+Xmin(ii);endendendend%%%%%%%%%%%%%%%%%%%%记录历代全局最优值%%%%%%%%%%%%%%%%%%%%%Convergence_curve(i)=gbest;%记录训练集的适应度值%% 清空环境变量warning off % 关闭报警信息close all% 关闭开启的图窗clear % 清空变量clc % 清空命令行ticrestoredefaultpath%% 导入数据P_train = xlsread('data','training set','B2：G191')';T_train= xlsread('data','training set','H2：H191')';% 测试集——44个样本P_test=xlsread('data','test set','B2:G45')';T_test=xlsread('data','test set','H2:H45')';%% 划分训练集和测试集M = size(P_train, 2);N = size(P_test, 2);f_ = size(P_train, 1);%% 数据归一化[p_train, ps_input] = mapminmax(P_train, 0, 1);p_test = mapminmax('apply', P_test, ps_input);[t_train, ps_output] = mapminmax(T_train, 0, 1);t_test = mapminmax('apply', T_test, ps_output);%% 参数初始化pop=10; %种群数量Max_iter=30; % 设定最大迭代次数dim = 2;% 维度为2，即优化两个超参数lb = [1,1];%下边界ub = [20,20];%上边界fobj = @(x) fun(x,p_train,t_train);[Best_pos,Best_score,curve]=PSO(pop,Max_iter,lb,ub,dim,fobj); %开始优化%% 提取最优参数n_trees = Best_pos(1);n_layer = Best_pos(2);%% 转置以适应模型p_train = p_train'; p_test = p_test';t_train = t_train'; t_test = t_test';%% 创建模型model = regRF_train(p_train, t_train, n_trees, n_layer);