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目录
📋1 概述
📝2 结果
📃3 参考文献
📋4 Matlab代码实现
📋1 概述
本文利用量子粒子群优化（QPSO）求解考虑单级最大/最大价格惩罚因子的立方准则函数提出的多目标组合经济排放调度（CEED）问题。QPSO在6单元发电系统上实现，并与拉格朗日松弛，粒子群优化（PSO）和模拟退火（SA）进行比较。所得结果验证了QPSO方法的有效性，证明了QPSO方法的鲁棒性。这项研究表明，QPSO可以作为其他电力调度问题的有效和强大的工具。
📝2 结果
global data edata w B B0 B00 Pd
data=[0.0010 0.0920 14.50 -136.00 50 200
0.0004 0.0250 22.00 -3.50 20 80
0.0006 0.0750 23.00 -81.00 15 50
0.0002 0.1000 13.50 -14.50 10 50
0.0013 0.1200 11.50 -9.75 10 50
0.0004 0.0840 12.50 75.60 12 40
];
edata=[0.0015 0.0920 14.0 -16.0
0.0014 0.0250 12.5 -93.5
0.0016 0.0550 13.5 -85.0
0.0012 0.0100 13.5 -24.5
0.0023 0.0400 21.0 -59.0
0.0014 0.0800 22.0 -70.0
];
B=1e-4*[0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
];%B=0.01*[0.06760 0.00953 -0.00507;0.00953 0.05210 0.00910;-0.00507 0.00901 0.02940];
B0=0.0*[0 0 0 0 0 0];
B00=000*.040357;
w=0.5;
Pd=150;
Pd=Pd+B00;
l=data(:,5)';
u=data(:,6)';
lu=[l' u'];
lu= lu';
range = lu(2,:) - lu(1,:);
for run=1:runno
mean=0;
T=cputime;
%x=(data(:,6) - data(:,5))*rand(popsize,dimension,1) + data(:,5)';
%for dimension=1
%x=(200 - 50)*rand(popsize,dimension,1) + 50;
%for dimension=2
%x=(80 - 20)*rand(popsize,dimension,1) + 20;
%end
%for dimension=3
%x=(50 - 15)*rand(popsize,dimension,1) + 15;
%end
%for dimension=4
%x=(50 - 10)*rand(popsize,dimension,1) + 10;
%end
%for dimension=5
%x=(50 - 10)*rand(popsize,dimension,1) + 10;
%end
%for dimension=6
%x=(40- 12)*rand(popsize,dimension,1) + 12;
%end
x=rand(popsize,dimension,1) ;
%end
for k = 1:dimension
x(:,k) = x(:,k)*range(k)+lu(1,k);
end
pbest=x;
gbest=zeros(1,dimension);
for i=1:popsize
f_x(i)=f6_cubic_ELD_mobjective(x(i,:));
f_pbest(i)=f_x(i);
end
g=min(find(f_pbest==min(f_pbest(1:popsize))));%g=find(f_pbest==min(f_pbest(1:popsize)));
gbest=pbest(g,:);
f_gbest=f_pbest(g);
MINIUM=f_pbest(g);
for t=1:MAXITER
disp('Minimum=');
disp(MINIUM);
disp('Iteration=');
disp(t);
beta=(w2-w1)*(MAXITER-t)/MAXITER+w1;
mbest=sum(pbest)/popsize;
for i=1:popsize
for j=1:6
data2(j,run)=gbest(j);%(i,:);
end;
%n=size(nest,1);
%beta=3/2;
%sigma=(gamma(1+beta)*sin(pi*beta/2)/(gamma((1+beta)/2)*beta*2^((beta-1)/2)))^(1/beta);
%for j=1:n,
%s=nest(j,:);
%u=randn(size(s))*sigma;
%=randn(size(s));
%step=u./abs(v).^(1/beta);
fi=rand(1,dimension);
p=fi.*pbest(i,:)+(1-fi).*gbest;
u=rand(1,dimension);
b=beta*abs(mbest-x(i,:));
v=-log(u);
%y=p+((-1).^ceil(w1+u)).*b.*v;%
y=p+((-1).^ceil(w1+rand(1,dimension))).*b.*v;
x(i,:)=y;
%x(i,:)=sign(y).*min(abs(y),xmax);
for j=1:1;
if x(i,j)<50;
x(i,j)=50;
end;
if x(i,j)>200.00;
x(i,j)=200.00;
end;
%x(i,j)=round(x(i,j));
end;
for j=2:2;
if x(i,j)<20;
x(i,j)=20;
end;
if x(i,j)>80.00;
x(i,j)=80.00;
end;
%x(i,j)=round(x(i,j));
end;
for j=3:3;
if x(i,j)<15;
x(i,j)=15;
end;
if x(i,j)>50.00;
x(i,j)=50.00;
end;
%x(i,j)=round(x(i,j));
end;
for j=4:5;
if x(i,j)<10;
x(i,j)=10;
end;
if x(i,j)>50.00;
x(i,j)=50.00;
end;
%x(i,j)=round(x(i,j));
end;
for j=6:6;
if x(i,j)<12;
x(i,j)=12;
end;
if x(i,j)>40;
x(i,j)=40;
end;
%x(i,j)=round(x(i,j));
end
f_x(i)=f6_cubic_ELD_mobjective(x(i,:));
if f_x(i)<f_pbest(i)
pbest(i,:)=x(i,:);
f_pbest(i)=f_x(i);
end
if f_pbest(i)<f_gbest
gbest=pbest(i,:);
f_gbest=f_pbest(i);
end
MINIUM=f_gbest;
end
disp('Power Output=');
disp(gbest);
data1(run,t)=MINIUM;
if MINIUM<2500
mean=mean+1;
end
end
sum1=sum1+mean;
sum2=sum2+MINIUM;
%MINIUM
time=cputime-T;
totaltime=totaltime+time;
📃3 参考文献
[1]Fahad Mahdi (2022). Quantum Particle Swarm Optimization for Multi-objective Combined Economic Emission Dispatch Problem.
