?

?

?

clc
clear
%?

time = cputime;

% computering run time
w = 1;?? ??? ?% inertia weight
c1 = 2;?? ??? ?% acceleration
c2 = 2;?? ??? ?% acceleration
eta =0.9;?? ?% ?updatingthe value of inertia weight in every iteration
%global ?Npop ;?? ?% particles number
Npop ?= 25 ? ;
%Vmax = 25;?? ?% the max value of velocity
%Vmin = 0;?? ?% the min value of velocity
%threshold = 1;?? ?% 
itera = 1;?? ?% the times of iteration

rand('state',sum(100*clock));?? ?% generate variable random numbers with time

% initialization
x1 = rand(Npop, 1) ? ? ? + 2.6; ? ? ? ? ? ? % ?x1 = [2.6, 3.6]
x2 = rand(Npop, 1)*0.1 ? + 0.7; ? ? ? ? ? ? % ?x2 = [0.7, 0.8]
x3 = round(rand(Npop, 1) * 11 + 17); ? ? ? ?% ?x3 = [17, 28] integer value
x4 = rand(Npop, 1) ? ? ? + 7.3; ? ? ? ? ? ? % ?x4 = [7.3, 8.3]
x5 = rand(Npop, 1) ? ? ? + 7.3; ? ? ? ? ? ? % ?x5 = [7.3, 8.3]
x6 = rand(Npop, 1) ? ? ? + 2.9; ? ? ? ? ? ? % ?x6 = [2.9, 3.9]
x7 = rand(Npop, 1)*0.5 ? + 5.0; ? ? ? ? ? ? % ?x7 = [5.0, 5.5]

v1max = 0.3*( max(x1) - min(x1));
v2max = 0.3*( max(x2) - min(x2));
v3max = 0.3*( max(x3) - min(x3));
v4max = 0.3*( max(x4) - min(x4));
v5max = 0.3*( max(x5) - min(x5));
v6max = 0.3*( max(x6) - min(x6));
v7max = 0.3*( max(x7) - min(x7));

v1 = rand(Npop, 1) * v1max;?? ?% 
v2 = rand(Npop, 1) * v2max;?? ?%?
v3 = rand(Npop, 1) * v3max;?? ?%?
v4 = rand(Npop, 1) * v4max;?? ?%?
v5 = rand(Npop, 1) * v5max;?? ?%?
v6 = rand(Npop, 1) * v6max;?? ?%?
v7 = rand(Npop, 1) * v7max;?? ?%?
% computing the fitness value of every particles
fit = fitness(x1, x2, x3, x4, x5, x6, x7,itera);

%?
%pbest = [x1 x2 x3 x4 x5 fit]; ? ? ? ? ? ? ? ?% pbest
pworst = [x1 x2 x3 x4 x5 x6 x7 fit];

%?
%[maxfit, indfit] = max(fit); ? ? ? ? ? ? ? ?% find the particle whose fitness value is the max
%gbest = [x1(indfit) x2(indfit) x3(indfit) x4(indfit) x5(indfit) maxfit]; ? ? % take that particle as the gbest particle

%?
[minfit, indfit] = min(fit); ? ? ? ? ? ? ? ?% find the particle whose fitness value is the min
gworst = [x1(indfit) x2(indfit) x3(indfit) x4(indfit) x5(indfit) x6(indfit) x7(indfit) minfit];?? ?% take that particle as the gworst

%the error between the gbest and gworst
%error = (maxfit - minfit)/maxfit; ? ? ? ? ? % 

% -------------------------------------------------------------------------
% --◆initialization complete
G_BEST = zeros(1,401);
ITERA_N =1000;
itera = 0;
Diversity = zeros(ITERA_N+1,1);
dir ? ? ? = ? ?1;
DivH ? ? ?= ? 0.001;
DivL ? ? ?= ? 0.00005;

while itera <= ITERA_N, ? ? ? ? ? ? ? ? ? ? ? ?% start iteration

?? ?itera = itera + 1;?? ??? ?%  the times of iteration
? ??
? ? Diversity(itera) ?= ?(var(x1) + var(x2) + var(x3) + var(x4) + var(x5) + var(x6) + var(x7))/Npop ;
?

? ??
? ? h1 = rand;?? ?h2 = rand;?? ?% random number: h1 and ?h2
? ?% if Diversity(itera) > DivH ?& dir < 0
? ?% ? ? dir = 1;?
? ?% elseif Diversity(itera) < DivL & dir > 0
? ? % ? ?dir = -1;
? % ?end
? ? ? ??
? ? ? ??
? ? ? ? v1 = w * v1 + dir*(c1 * h1 * (pworst(:,1) - x1) + c2 * h2 * (gworst(1) - x1));?? ?% updatingv1
? ? ? ? v2 = w * v2 + dir*(c1 * h1 * (pworst(:,2) - x2) + c2 * h2 * (gworst(2) - x2));?? ?% ? updating ?v2
? ? ?? ?v3 = w * v3 + dir*(c1 * h1 * (pworst(:,3) - x3) + c2 * h2 * (gworst(3) - x3));?? ?% ? updating ?v3
? ? ? ? v4 = w * v4 + dir*(c1 * h1 * (pworst(:,4) - x4) + c2 * h2 * (gworst(4) - x4));?? ?% ? updating ?v4
? ? ? ? v5 = w * v5 + dir*(c1 * h1 * (pworst(:,5) - x5) + c2 * h2 * (gworst(5) - x5)); ?% ? updating ?v5
? ? ? ? v6 = w * v6 + dir*(c1 * h1 * (pworst(:,6) - x6) + c2 * h2 * (gworst(6) - x6)); ?% ? updating ?v6
? ? ? ? v7 = w * v7 + dir*(c1 * h1 * (pworst(:,7) - x7) + c2 * h2 * (gworst(7) - x7)); ?% ? updating ?v7

? ??
? ? v1(find( v1 > v1max)) = v1max; ?v1(find( v1 < -v1max)) = -v1max;
? ? v2(find( v2 > v2max)) = v2max; ?v2(find( v2 < -v2max)) = -v2max;
? ? v3(find( v3 > v3max)) = v3max; ?v3(find( v3 < -v3max)) = -v3max;
? ? v4(find( v4 > v4max)) = v4max; ?v4(find( v4 < -v4max)) = -v4max;
? ? v5(find( v5 > v5max)) = v5max; ?v5(find( v5 < -v5max)) = -v5max;
? ? v6(find( v6 > v6max)) = v6max; ?v6(find( v6 < -v6max)) = -v6max;
? ? v7(find( v7 > v7max)) = v7max; ?v7(find( v7 < -v7max)) = -v7max;
? ??
? ??
? ??
?? ?x1 = x1 + v1;?? ? ? ? ? ?% updating x1
?? ?x2 = x2 + v2;?? ? ? ? ? ?% updating x2
? ? x3 = round(x3 + v3); ? ?% updating x3,integer value
? ? x4 = x4 + v4;?? ? ? ? ? ?% updating x4
? ? x5 = x5 + v5;?? ? ? ? ? ?% updating x5
? ? x6 = x6 + v6;?? ? ? ? ? ?% updating x6
? ? x7 = x7 + v7;?? ? ? ? ? ?% updating x7
? ??
? ? x1(find( x1 > 3.6 )) ?= 3.6; x1(find( x1 < 2.6 )) = 2.6; ? % 
? ? x2(find( x2 > 0.8 )) ?= 0.8; x2(find( x2 < 0.7 )) = 0.7; ? % make the values of x1, x2, x3, x4, x5 limited in its region
? ? x3(find( x3 > 28 ?)) ?= 28 ; x3(find( x3 < 17 )) ?= 17;
? ? x4(find( x4 > 8.3 )) ?= 8.3; x4(find( x4 < 7.3 )) = 7.3;
? ? x5(find( x5 > 8.3 )) ?= 8.3; x5(find( x5 < 7.3 )) = 7.3;
? ? x6(find( x6 > 3.9 )) ?= 3.9; x6(find( x6 < 2.9 )) = 2.9; ??
? ? x7(find( x7 > 5.5 )) ?= 5.5; x7(find( x7 < 5.0 )) = 5.0;
?? ?% updating pbest and gbest 
?? ?[new_fit ]= fitness(x1, x2, x3, x4, x5, x6 ,x7,itera); ? ? ? ? ? ? ? ? ??? ?% computing every particles' fitness value

? ? %for i = 1 : Npop, ? % 1:40
?? ?%?? ?if ?new_fit(i) > pbest(i,6),
?? ?%?? ??? ?pbest(i,:) = [x1(i) x2(i) x3(i) x4(i) x5(i) new_fit(i)];?? ?% updating pbest 
? ? % ? ?end
? ? %end
? ??
? ? for i = 1 : Npop, ? % 1:40
?? ??? ?if ?new_fit(i) < pworst(i,8),
?? ??? ??? ?pworst(i,:) = [x1(i) x2(i) x3(i) x4(i) x5(i) x6(i) x7(i) new_fit(i)];?? ?% updating pbest 
? ? ? ? end
? ? end
? ??
? ??
%?
% [maxfit, indfit] = max(fit); ? ? ? ? ? ? ? ?%
% gbest = [x1(indfit) x2(indfit) maxfit];

%?? ?[maxfit(itera), indfit] = max(new_fit); ? ? ? ? ? ? % find the max fitness value in this iteration 
%?? ?if maxfit(itera) > gbest(3),
%?? ??? ?gbest = [x1(indfit) x2(indfit) maxfit(itera)];?? ?% updating gbest ?? ?
%?? ?end;

? ??
? ? [minfit(itera), indfit] = min(new_fit); ? ? ? ? ? ? % find the min fitness value in this iteration 
?? ?if minfit(itera) < gworst(8)
?? ??? ?gworst = [x1(indfit) x2(indfit) x3(indfit) x4(indfit) x5(indfit) x6(indfit) x7(indfit) minfit(itera)];?? ?% updating gworst
?? ?end;
?
? ? G_BEST(itera) = gworst(8);
? ??
?? ?% ?
?? ?avgfit(itera) = mean(new_fit);?? ?%computing the average fitness value of all particles?

?? ?% ?error
%?? ?error = (maxfit(itera)- minfit(itera))/maxfit(itera);?? ??? ??? ?% 
? ??
? ? %  w 
? ? w = w * eta;

% plotting result

%subplot(2,1,1)
%plot(x1, x2, '.',gbest(1),gbest(2),'r.')
%axis([-500 500 -500 500])
%title('The distribution of particles')
%xlabel('x position');
%ylabel('y position');

%subplot(2,1,2)

end;
%--------------------------------------◆
figure(1)
t = 1 : ITERA_N+1;?? ??? ?%?
plot(t, minfit,'b', t, avgfit,'g')
hold on
plot(t,G_BEST,'r','LineWidth',3);

axis([0 ITERA_N+10 ?2500 5000]);
title('fitness values');
xlabel('itera');
ylabel('fitness values');
legend('MinFit','AvgFit','Gbest')
x1 ? ? ?= ? ? ?gworst(1)*ones(40,1);
x2 ? ? ?= ? ? ?gworst(2)*ones(40,1);
x3 ? ? ?= ? ? ?gworst(3)*ones(40,1);
x4 ? ? ?= ? ? ?gworst(4)*ones(40,1);
x5 ? ? ?= ? ? ?gworst(5)*ones(40,1);
x6 ? ? ?= ? ? ?gworst(6)*ones(40,1);
x7 ? ? ?= ? ? ?gworst(7)*ones(40,1);

g1 ? ? ?= ? ? ?gcon1(x1, x2, x3);
g2 ? ? ?= ? ? ?gcon2(x1, x2, x3);
g3 ? ? ?= ? ? ?gcon3(x2, x3, x4, x6);
g4 ? ? ?= ? ? ?gcon4(x2, x3, x5, x7);
g5 ? ? ?= ? ? ?gcon5(x2, x3, x4, x6);
g6 ? ? ?= ? ? ?gcon6(x2, x3, x5, x7);
g7 ? ? ?= ? ? ?gcon7(x2, x4);
g8 ? ? ?= ? ? ?gcon8(x1, x2);
g9 ? ? ?= ? ? ?gcon9(x1, x2);
g10 ? ? = ? ? ?gcon10(x4, x6);
g11 ? ? = ? ? ?gcon11(x5, x7);

time = cputime - time
D131

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