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问题描述仿真过程matlab源码
问题描述
仿真过程
matlab源码
%该脚本要命名为func3.m
%%%%%%%%%%%%%%%%%%%%%%%适应度函数%%%%%%%%%%%%%%%%%%%%%%%%%
function y=func3(x)
y=-((x(1).^2+x(2)-1).^2+(x(1)+x(2).^2-7).^2)/200+10;
200928lu注：该matlab代码成功在matlabR2019a运行
%%%%%%%%%%%%%%%%%离散差分进化算法求函数极值%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%初始化%%%%%%%%%%%%%%%%%%%%%%%%
clear all; %清除所有变量
close all; %清图
clc; %清屏
NP=20; %个体数目
D=2; %变量的维数
G=100; %最大进化代数
F=0.5; %变异算子
CR=0.1; %交叉算子
Xs=100; %上限
Xx=-100; %下限
%%%%%%%%%%%%%%%%%%%%%%%%%赋初值%%%%%%%%%%%%%%%%%%%%%%%%
x=zeros(D,NP); %初始种群
v=zeros(D,NP); %变异种群
u=zeros(D,NP); %选择种群
% x=randint(D,NP,[Xx,Xs]); %赋初值
x=randi([Xx,Xs],D,NP);
%%%%%%%%%%%%%%%%%%%%计算目标函数%%%%%%%%%%%%%%%%%%%%%%%
for m=1:NP
Ob(m)=func3(x(:,m));
end
trace(1)=max(Ob);
%%%%%%%%%%%%%%%%%%%%%%%差分进化循环%%%%%%%%%%%%%%%%%%%%%
for gen=1:G
%%%%%%%%%%%%%%%%%%%%%%变异操作%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%r1,r2,r3和m互不相同%%%%%%%%%%%%%%%
for m=1:NP
% r1=randint(1,1,[1,NP]);
r1=randi([1,NP],1,1);
while (r1==m)
% r1=randint(1,1,[1,NP]);
r1=randi([1,NP],1,1);
end
% r2=randint(1,1,[1,NP]);
r2=randi([1,NP],1,1);
while (r2==m)|(r2==r1)
% r2=randint(1,1,[1,NP]);
r2=randi([1,NP],1,1);
end
% r3=randint(1,1,[1,NP]);
r3=randi([1,NP],1,1);
while (r3==m)|(r3==r1)|(r3==r2)
% r3=randint(1,1,[1,NP]);
r3=randi([1,NP],1,1);
end
v(:,m)=floor(x(:,r1)+F*(x(:,r2)-x(:,r3))); 201001lu注：离散差分进化算法的变异算子
end
%%%%%%%%%%%%%%%%%%%%%%交叉操作%%%%%%%%%%%%%%%%%%%%%%%
% r=randint(1,1,[1,D]);
r=randi([1,D],1,1);
for n=1:D
cr=rand(1);
if (cr<=CR)|(n==r)
u(n,:)=v(n,:);
else
u(n,:)=x(n,:);
end
end
%%%%%%%%%%%%%%%%%%%边界条件的处理%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%边界吸收%%%%%%%%%%%%%%%%%%%%%%%%%
for n=1:D
for m=1:NP
if u(n,m)<Xx
u(n,m)=Xx;
end
if u(n,m)>Xs
u(n,m)=Xs;
end
end
end
%%%%%%%%%%%%%%%%%%%%%%选择操作%%%%%%%%%%%%%%%%%%%%%%%
for m=1:NP
Ob1(m)=func3(u(:,m));
end
for m=1:NP
if Ob1(m)>Ob(m)
x(:,m)=u(:,m);
end
end
for m=1:NP
Ob(m)=func3(x(:,m));
end
trace(gen+1)=max(Ob);
end
[SortOb,Index]=sort(Ob);
X=x(:,Index);
Xbest=X(:,end); %最优变量
Y=max(Ob); %最优值
%%%%%%%%%%%%%%%%%%%%%%%%%画图%%%%%%%%%%%%%%%%%%%%%%%%%%
figure
plot(trace);
xlabel('迭代次数')
ylabel('目标函数值')
title('DE目标函数曲线')
此处为得到的优化结果
>> Xbest=X(:,end)
Xbest =
-2
-3
>> Y=max(Ob)
Y =
10
中 智能优化算法及其MATLAB实例（第二版）[包子阳，余继周][电子工业出版社][2018年01月][9787121330308]
