function [best,fmin]=CPA(Lb,Ub,Dim,opt,tol)
obj=zeros(1,tol);
%Define CPA parameters values
group_iter = 2;
attraction_rate = 0.8;
growth_rate = 2;
reproduction_rate = 1.8;
nCP = 10;
nPrey = 20;
nPop=nCP+nPrey;
% Initialize Carnivorous Plants and their surrounding food
ini.position=[];
ini.cost=[];
life=repmat(ini,nPop,1);
NewCP=repmat(ini,nCP*group_iter+nCP,1);
life=CreateInitialPopulation(life,Dim,Ub,Lb);
%Find the current best
for x=1:nCP+nPrey
costs(x,:)=life(x).cost;
end
[fmin,I]=min(costs);
best=life(I).position;
%Find the distance between best and optima solution for 1st row
distance=dist(best,opt);
N_iter=1;
%Start the iterations
for t=1:tol
% Group the Carnivorous Plants with their nearby surrounding foods
% based on distance.
[CP,life]=CPA_Grouping(life,nCP,nPrey,group_iter,N_iter);
% Grow new Carnivorous Plants by hunting preys and birth new preys
% if it is not being hunted.
[NewCP,a]=CPA_Growth(CP,NewCP,Dim,attraction_rate,growth_rate,group_iter);
% Mating between Carnivorous Plants and the best Carnivorous Plants
NewCP=CPA_Reproduction(CP,NewCP,Dim,reproduction_rate,a);
% Check the position and update the cost of NewCP
NewCP=CPA_UpdateCost(NewCP,Dim,Lb,Ub);
% Combine NewCP and pop
[life,BestIndex]=CPA_Combine(NewCP,life);
% Update Best Solution Ever Found
BestSol=life(BestIndex);
%Find current global best
fmin=BestSol.cost;
best=BestSol.position;
%Find the distance between best and optima solution for the rest of
%the row
distance=dist(best,opt);
N_iter=N_iter+1;
obj(t) =fmin;
plot(obj(1:t),'r','LineWidth',2);
xlabel('迭代次数')
ylabel('最优解')
title('食肉植物优化算法')
drawnow
% if N_iter >= 14000
% break;
% end
end
ElapsedTime=toc;
% Output/display the final result
disp(['Total computational time used is ',num2str(ElapsedTime),' seconds'])
disp(['Total number of function evaluation = ',num2str(nPop+(N_iter-1)*(nCP*group_iter+nCP))])
disp(['Best solution = ',num2str(best,'% .8f')])
disp(['Fmin = ',num2str(fmin,'% .8f')])
function [CP,pop]=CPA_Grouping(pop,nCP,nPrey,Group_Iter,it)
empty_Prey.position=[];
empty_Prey.cost=[];
Select=reshape(1:nPrey,nCP,[]);
% First, sort the population
if it==1
for x=1:nCP+nPrey
costs(x,:)=pop(x).cost;
end
else
for x=1:(2+Group_Iter)*nCP+nPrey
costs(x,:)=pop(x).cost;
end
end
for i=1:nCP+nPrey
[~, I]=min(costs);
index(i)=I;
costs(I)=10^30;
end
pop=pop(index);
Plant=pop(1:nCP);
Prey=pop(nCP+1:end);
empty_CP.Plant=[];
empty_CP.Prey=repmat(empty_Prey,0,1);
empty_CP.nPrey=0;
CP=repmat(empty_CP,nCP,1);
% Group the Carnivorous Plants with their nearby Preys
for q=1:nCP
CP(q).Plant=Plant(q);
for r=1:nPrey/nCP
CP(q).Prey=[CP(q).Prey
Prey(Select(q,r))];
CP(q).nPrey=CP(q).nPrey+1;
end
end
function [NewCP,a]=CPA_Growth(CP,NewCP,Dim,HuntingChance,alpha,Group_Iter)
a=1;
nCP=numel(CP);
for Grp=1:nCP
for Group_cycle=1:Group_Iter
v=randi(CP(Grp).nPrey);
if HuntingChance>rand %Growth of Carnivorous Plant by hunting prey
Step=alpha.*rand(1,Dim);
NewCP(a).position=Step.*CP(Grp).Plant.position...
+(1-Step).*CP(Grp).Prey(v).position;
a=a+1;
else %Mating of Prey
%To ensure prey v and prey j are different
u=v;
while v==u
u=randi(CP(Grp).nPrey);
end
Step=alpha.*rand(1,Dim);
if CP(Grp).Prey(v).cost<CP(Grp).Prey(u).cost
Step=1-Step; %So that it moves to good prey
end
NewCP(a).position=Step.*CP(Grp).Prey(u).position...
+(1-Step).*CP(Grp).Prey(v).position;
a=a+1;
end
end
end
function NewCP=CPA_Reproduction(CP,NewCP,Dim,alpha,a)
%Mating of Carnivorous Plant
for i=1:numel(CP)
for j=1:Dim
%To ensure plant j and plant v are different
v=i;
while v==i
v=randi(numel(CP));
end
Step=CP(i).Plant.position(j)-CP(v).Plant.position(j);
if CP(v).Plant.cost<CP(i).Plant.cost
Step=-Step; %So that it moves to good plant
end
NewCP(a).position(j)=CP(1).Plant.position(j)+alpha.*rand.*(Step);
end
a=a+1;
end
function NewCP=CPA_UpdateCost(NewCP,Dim,Lb,Ub)
n=numel(NewCP);
for i=1:n
Flag4ub=NewCP(i).position>Ub;
Flag4lb=NewCP(i).position<Lb;
NewCP(i).position=(NewCP(i).position.*(~(Flag4ub+Flag4lb)))...
+(rand(1,Dim).*(Ub-Lb)+Lb).*(Flag4ub+Flag4lb);
NewCP(i).cost=Fun(NewCP(i).position,Dim);
end
function [pop,BestIndex]=CPA_Combine(NewCP,pop)
pop=[pop
NewCP];
for i=1:size(pop,1)
costs(i,:)=pop(i).cost;
end
[~,BestIndex]=min(costs);