求，人工鱼群算法优化PID参数（我有传递函数），要有代码，simulink图，有偿！！！
1. 确定自变量即取值范围：PID的三个参数值，并确定取值范围。
2. 目标函数：定义输出跟踪的积分性能指标，如IAE值。
3. 按照下面的人工鱼群算法代码进行优化。
%sum(sin(x)./x) 极小值
clear all;
close all;
clc;
Visual = 25; %人工鱼的感知距离
Step = 3; %人工鱼的移动最大步长
N = 30; %人工鱼的数量
dim=10; %人工鱼维度
Try_number = 50;%迭代的最大次数
delta=27; %拥挤度因子
%测试函数
f=@(x) sum(x.^2);
ub=100;%边界上限
lb=-100;%边界下限
d = [];%存储50个状态下的目标函数值;
Iteration = 1; %
Max_iteration = 500;%迭代次数
%初始化人工鱼种群
x=lb+rand(N,dim).*(ub-lb);
%计算10个初始状态下的适应度值；
for i = 1:N
fitness_fish(i) = f(x(i,:));
end
[best_fitness,I] = min(fitness_fish); % 求出初始状态下的最优适应度；
best_x = x(I,:); % 最优人工鱼；
while Iteration<=Max_iteration
for i = 1:N
%% 聚群行为
nf_swarm=0;
Xc=0;
label_swarm =0; %群聚行为发生标志
%确定视野范围内的伙伴数目与中心位置
for j = 1:N
if norm(x(j,:)-x(i,:))<Visual
nf_swarm = nf_swarm+1; %统计在感知范围内的鱼数量
Xc = Xc+x(j,:); %将感知范围内的鱼进行累加
end
end
Xc=Xc-x(i,:); %需要去除本身；因为在 一开始计算时，i=j，把中心的鱼也进行了一次计算
nf_swarm=nf_swarm-1;
Xc = Xc/nf_swarm; %此时Xc表示视野范围其他伙伴的中心位置；
%判断中心位置是否拥挤
if (f(Xc)/nf_swarm < delta*f(x(i,:))) && (f(Xc)<f(x(i,:)))
x_swarm=x(i,:)+rand*Step.*(Xc-x(i,:))./norm(Xc-x(i,:));
%边界处理
ub_flag=x_swarm>ub;
lb_flag=x_swarm<lb;
x_swarm=(x_swarm.*(~(ub_flag+lb_flag)))+ub.*ub_flag+lb.*lb_flag;
x_swarm_fitness=f(x_swarm);
else
%觅食行为
label_prey =0; %判断觅食行为是否找到优于当前的状态
for j = 1:Try_number
%随机搜索一个状态
x_prey_rand = x(i,:)+Visual.*(-1+2.*rand(1,dim));
ub_flag2=x_prey_rand>ub;
lb_flag2=x_prey_rand<lb;
x_prey_rand=(x_prey_rand.*(~(ub_flag2+lb_flag2)))+ub.*ub_flag2+lb.*lb_flag2;
%判断搜索到的状态是否比原来的好
if f(x(i,:))>f(x_prey_rand)
x_swarm = x(i,:)+rand*Step.*(x_prey_rand-x(i,:))./norm(x_prey_rand-x(i,:));
ub_flag2=x_swarm>ub;
lb_flag2=x_swarm<lb;
x_swarm=(x_swarm.*(~(ub_flag2+lb_flag2)))+ub.*ub_flag2+lb.*lb_flag2;
x_swarm_fitness=f(x_swarm);
label_prey =1;
break;
end
end
%随机行为
if label_prey==0
x_swarm = x(i,:)+Step*(-1+2*rand(1,dim));
ub_flag2=x_swarm>ub;
lb_flag2=x_swarm<lb;
x_swarm=(x_swarm.*(~(ub_flag2+lb_flag2)))+ub.*ub_flag2+lb.*lb_flag2;
x_swarm_fitness=f(x_swarm);
end
end
%% 追尾行为
fitness_follow = inf;
label_follow =0;%追尾行为发生标记
%搜索人工鱼Xi视野范围内的最高适应度个体Xj
for j = 1:N
if (norm(x(j,:)-x(i,:))<Visual) && (f(x(j,:))<fitness_follow)
best_pos = x(j,:);
fitness_follow = f(x(j,:));
end
end
%搜索人工鱼Xj视野范围内的伙伴数量
nf_follow=0;
for j = 1:N
if norm(x(j,:)-best_pos)<Visual
nf_follow=nf_follow+1;
end
end
nf_follow=nf_follow-1;%去掉他本身
%判断人工鱼Xj位置是否拥挤
if (fitness_follow/nf_follow)<delta*f(x(i,:)) && (fitness_follow<f(x(i,:)))
x_follow = x(i,:)+rand*Step.*(best_pos-x(i,:))./norm(best_pos-x(i,:));
%边界判定
ub_flag2=x_follow>ub;
lb_flag2=x_follow<lb;
x_follow=(x_follow.*(~(ub_flag2+lb_flag2)))+ub.*ub_flag2+lb.*lb_flag2;
label_follow =1;
x_follow_fitness=f(x_follow);
else
%觅食行为
label_prey =0; %判断觅食行为是否找到优于当前的状态
for j = 1:Try_number
%随机搜索一个状态
x_prey_rand = x(i,:)+Visual.*(-1+2.*rand(1,dim));
ub_flag2=x_prey_rand>ub;
lb_flag2=x_prey_rand<lb;
x_prey_rand=(x_prey_rand.*(~(ub_flag2+lb_flag2)))+ub.*ub_flag2+lb.*lb_flag2;
%判断搜索到的状态是否比原来的好
if f(x(i,:))>f(x_prey_rand)
x_follow = x(i,:)+rand*Step.*(x_prey_rand-x(i,:))./norm(x_prey_rand-x(i,:));
ub_flag2=x_follow>ub;
lb_flag2=x_follow<lb;
x_follow=(x_follow.*(~(ub_flag2+lb_flag2)))+ub.*ub_flag2+lb.*lb_flag2;
x_follow_fitness=f(x_follow);
label_prey =1;
break;
end
end
%随机行为
if label_prey==0
x_follow = x(i,:)+Step*(-1+2*rand(1,dim));
ub_flag2=x_follow>ub;
lb_flag2=x_follow<lb;
x_follow=(x_follow.*(~(ub_flag2+lb_flag2)))+ub.*ub_flag2+lb.*lb_flag2;
x_follow_fitness=f(x_follow);
end
end
% 两种行为找最优
if x_follow_fitness<x_swarm_fitness
x(i,:)=x_follow;
else
x(i,:)=x_swarm;
end
end
%% 更新信息
for i = 1:N
if (f(x(i,:))<best_fitness)
best_fitness = f(x(i,:));
best_x = x(i,:);
end
end
Convergence_curve(Iteration)=best_fitness;
Iteration = Iteration+1;
if mod(Iteration,50)==0
display(['迭代次数：',num2str(Iteration),'最优适应度：',num2str(best_fitness)]);
display(['最优人工鱼：',num2str(best_x)]);
end
end
figure('Position',[284 214 660 290])
subplot(1,2,1);
x=-100:1:100; y=x;
L=length(x);
for i=1:L
for j=1:L
F(i,j)=x(i).^2+y(j).^2;
end
end
surfc(x,y,F,'LineStyle','none');
title('Test function')
xlabel('x_1');
ylabel('x_2');
zlabel(['sum','( x_1 , x_2 )'])
grid off
subplot(1,2,2);
semilogy(Convergence_curve,'Color','b')
title('Convergence curve')
xlabel('Iteration');
ylabel('Best fitness');
axis tight
grid off
box on
请问楼主怎么解决的呀，算法怎么和simulink仿真模型联合呢