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Fitness.m

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+function [Error] = Fitness(R, Hfilt, Wfilt, N, Ord, Logcoef, alpha, beta, gamma, Cst2)
+T1     = 0;
+T2     = 0;
+Cst    = R(1) + 1;
+Hfsiz  = size(Hfilt, 1);
+Rsiz1  = size(R, 1);
+Hfilt_N   = zeros(Hfsiz, 1);
+q_N       = zeros(Hfsiz, 1);
+F         = zeros(Hfsiz, 1);
+Error     = zeros(Rsiz1, 1);
+
+Rsiz2  = size(R,2) - Cst2;
+Bipo   = zeros(Rsiz1, Rsiz2/2);        
+Aipo   = zeros(Rsiz1, Rsiz2/2 + Cst2);      
+
+Aipo(:,1) = 1;
+for i = 1:Rsiz1
+    for j = 1:Rsiz2/2              
+        Bipo(i,j) = R(i,j + 1);
+%         Aipo(i,1 + j) = R(i,(Rsiz2/2) + j + 1);       %### For O < 5
+        if (j < Cst)                                    %### For O >= 5
+            Aipo(i,1 + j) = R(i,(Rsiz2/2) + j + 1);     %### For O >= 5
+        end                                             %### For O >= 5
+    end
+
+    [q,Q] = freqz(Bipo(i,:),Aipo(i,:),Hfsiz);
+
+
+Hfilt_N = Hfilt .* N;     % output with input white noise
+q_N     = q .* N;         % output with input white noise
+    for j = 1:Hfsiz
+     F(j,1) = (abs(Hfilt_N(j) - q_N(j)))^ 2;
+    end
+    P_f        = roots(Aipo);
+    T1         = sum(abs(P_f(find(abs(P_f) >= 1))));
+    T2         = sum(abs(P_f));
+    Error(i,1) = (alpha*((1/Hfsiz)*sum(F))) + (beta*((R(1)/(Ord-1)) * (T1/T2)));
+    Hfilt_N    = 0;
+    q_N        = 0;
+end
+end
+

Main_IIR.m

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+function [O h w] = Main_IIR()
+
+O = 5;
+b = [0.1084 0.5419 1.0837 1.0837 0.5419 0.1084];      %for case4
+a = [1 0.9853 0.9738 0.3864 0.1112 0.0113];           %for case4
+
+[h,w]=freqz(b,a,200);
+
+end
+

Matching.m

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+function [Bipo Aipo Z_f P_f] = Matching(R, Cst2)
+
+Rsiz = size(R,2) - Cst2;
+Cst    = R(1) + 1;
+
+Bipo      = zeros(1, Rsiz/2);   
+Aipo      = zeros(1, Rsiz/2 + Cst2);  
+Aipo(1,1) = 1;
+for i = 1:Rsiz/2           
+% for i = 1:5             
+    Bipo(1,i)   = R(i + 1);
+%     Aipo(1,i + 1) = R((Rsiz/2) + i + 1);    %### For O < 5    
+    if (i < Cst)                              %### For O >= 5
+        Aipo(1,i + 1) = R((Rsiz/2) + i + 1);  %### For O >= 5
+    end                                       %### For O >= 5        
+end
+Z_f = roots(Bipo);
+P_f = roots(Aipo);
+
+end
+
+
+
+

MonteCarlo.m

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+clc;
+clear all;
+close all
+tic
+
+%% IIR Filter Fitness
+
+[O Hfilt Wfilt] = Main_IIR();
+
+Logcoef = 1;
+alpha   = 0.6; % 0.4 1 0.5 0.5
+beta    = 0.4; % 0.6 0 0.0 0.1
+gamma   = 0.0; % 0.0 1 0.5 0.4
+Cst1    = 2; % number 1 for O<5 and 2 for >= for PSO function
+Cst2    = 0; % number 1 for O<5,0 for >= for Fitness/IIRSOA function
+nvar    = (2 * (O - 1)) + Cst1;
+pnvar   = 0;
+maxit   = 1000000;
+
+xmin(1)           = 1;
+xmax(1)           = (O - 1);
+xmin(2:nvar)      = repmat(-2, 1, nvar - 1);
+xmax(2:nvar)      = repmat(+2, 1, nvar - 1);
+Xpos              = zeros(maxit,nvar);
+Xfit              = zeros(maxit,1);
+N                 = rand(size(Hfilt,1),1);
+% gbest             = zeros(1,nvar);
+gbest             = 0;
+gbestfit          = inf;
+meanfit           = zeros(maxit,1);
+minfit            = zeros(maxit,1);
+
+%% Main loop
+
+for it = 1:maxit
+    Xpos(it,:) = xmin + (xmax - xmin) .* rand(1,nvar); 
+    Xpos(it,1) = round(Xpos(it,1));
+    pnvar      = (2 * Xpos(it,1)) + Cst1;
+    if pnvar < nvar
+        Xpos(it,pnvar+1:nvar) = 0;
+    end
+
+    %---------------------------------------------------------------------
+    Xfit(it)   = Fitness(Xpos(it,1:pnvar),Hfilt,Wfilt,N,O,Logcoef,alpha,beta,gamma,Cst2);
+    %---------------------------------------------------------------------
+
+    if Xfit(it) < gbestfit
+        gbest    = Xpos(it,1:pnvar);
+        gbestfit = Xfit(it);
+    end
+    meanfit(it) = sum(Xfit)/it;
+    minfit(it)  = min(Xfit(1:it));
+    D = it/maxit;
+    if ( D == 1/5) | ( D == 2/5) | ( D == 3/5) | ( D == 4/5)
+        it
+    end
+end
+
+time = toc
+gbest
+gbestfit
+Minimum_Fitness = minfit(it)
+Mean_Fitness    = meanfit(it)
+[Bsoa Asoa Z_f P_f] = Matching(gbest,Cst2)
+
+% xx = [1,100,200,300,400,500];
+% pdf(Xfit([1,100,200,300,400,500]),xx)
+
+figure(1);
+plot(1:maxit,meanfit,'r--','LineWidth',2)
+ylim([-1 inf]);
+% xlim([-1 inf]);
+legend('MeanFitness')
+xlabel('Iterations')
+ylabel('Value')
+title('Monte Carlo'); 
+grid on
+
+figure(2);
+plot(1:maxit,minfit,'b--','LineWidth',2);
+axis([0 inf -.01 inf])
+grid on
+legend('MinFitness')
+xlabel('Iterations')
+ylabel('Value')
+title('Monte Carlo');  
+
+figure(3);
+zplane(Z_f,P_f); %%% Displays the poles and zeros of discrete-time systems.
+legend('Zero','Pole');
+xlabel('Real Part');
+ylabel('Imaginary Part');
+title('MonteCarlo: Pole-Zero Plot');
+
+
+
+