matlab-(含教程)基于灰狼优化(GWO)的目标优化matlab仿真

clc; clear; close all; warning off; addpath(genpath(pwd)); rng('default'); % Problem Statement Npar = 3; VarLow=[-5.12 -5.12 -5.12]; VarHigh = [5.12 5.12 5.12]; % parameters WolfSize = 100; MaxIter = 100; % initialize a random value as best value alpha.Position=rand(1,Npar).* (VarHigh - VarLow) + VarLow; alpha.Fit = fitnessFunc(alpha.Position); GB=alpha.Fit; t=cputime; % Initialization of memory X = repmat(struct('Position',zeros(1,Npar),'Fit',zeros(1,Npar)),WolfSize,1); X(1) = alpha; % first wolf is alpha for ii = 2:WolfSize %initial position of wolves X(ii).Position= rand(1,Npar).* (VarHigh - VarLow) + VarLow; % fitness of the solutions X(ii).Fit=fitnessFunc(X(ii).Position); % evaluate alpha if (X(ii).Fit < alpha.Fit) alpha.Position = X(ii).Position; alpha.Fit = X(ii).Fit; end end % sort wolves according to fitness [~, sortind] = sort([X.Fit]); X = X(sortind); %initialize alpha beta and delta alpha = X(1); Beta = X(2); delta = X(3); %initialize A and C vectors a=2*ones(1,Npar); A1=2*rand(1,Npar).*a -a; A2=2*rand(1,Npar).*a -a; A3=2*rand(1,Npar).*a -a; C1=2*rand(1,Npar); C2=2*rand(1,Npar); C3=2*rand(1,Npar); % Main Loop for jj = 1:MaxIter rng(jj); for ii = 1:WolfSize %compute encircling vectors Da=abs(C1.*alpha.Position-X(ii).Position); Db=abs(C2.*Beta.Position-X(ii).Position); Dd=abs(C3.*delta.Position-X(ii).Position); %update wolf position X1=alpha.Position - A1.*Da; X2=Beta.Position - A2.*Db; X3=delta.Position - A3.*Dd; X(ii).Position = (X1 + X2 + X3)/3; %maintian constraints X(ii).Position=limiter(X(ii).Position,VarHigh,VarLow); %update fitness X(ii).Fit=fitnessFunc(X(ii).Position); % update alpha if better if X(ii).Fit < alpha.Fit alpha = X(ii); end end % sort wolves according to fitness [~, sortind] = sort([X.Fit]); X = X(sortind); %update beta and delta Beta = X(2); delta = X(3); %update A and C vectors a=2*(1-jj/MaxIter); A1=2*rand(1,Npar).*a -a; A2=2*rand(1,Npar).*a -a; A3=2*rand(1,Npar).*a -a; C1=2*rand(1,Npar); C2=2*rand(1,Npar); C3=2*rand(1,Npar); % store the best value in each iteration GB = [GB alpha.Fit]; end t1=cputime; fprintf('The time taken is %3.2f seconds \n',t1-t); fprintf('The best value is :'); alpha.Position alpha.Fit figure plot(0:MaxIter,GB, 'linewidth',1.2); xlabel('Iterations'); ylabel('Objective Function (Cost)'); grid('on')