gusucode.com > Toolbox_all_algorithms > Toolbox_all_algorithms/DA/DA.m
%___________________________________________________________________%
% Dragonfly Algorithm (DA) source codes demo version 1.0 %
% %
% Developed in MATLAB R2011b(7.13) %
% %
% Author and programmer: Seyedali Mirjalili %
% %
% e-Mail: ali.mirjalili@gmail.com %
% seyedali.mirjalili@griffithuni.edu.au %
% %
% Homepage: http://www.alimirjalili.com %
% %
% Main paper: %
% %
% S. Mirjalili, Dragonfly algorithm: a new meta-heuristic %
% optimization technique for solving single-objective, discrete, %
% and multi-objective problems, Neural Computing and Applications %
% DOI: http://dx.doi.org/10.1007/s00521-015-1920-1 %
% %
%___________________________________________________________________%
% You can simply define your cost in a seperate file and load its handle to fobj
% The initial parameters that you need are:
%__________________________________________
% fobj = @YourCostFunction
% dim = number of your variables
% Max_iteration = maximum number of generations
% SearchAgents_no = number of search agents
% lb=[lb1,lb2,...,lbn] where lbn is the lower bound of variable n
% ub=[ub1,ub2,...,ubn] where ubn is the upper bound of variable n
% If all the variables have equal lower bound you can just
% define lb and ub as two single number numbers
% To run DA: [Best_score,Best_pos,cg_curve]=DA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj)
%__________________________________________
function [Best_score,Best_pos,cg_curve]=DA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj,handles)
display('DA is optimizing your problem');
cg_curve=zeros(1,Max_iteration);
if size(ub,2)==1
ub=ones(1,dim)*ub;
lb=ones(1,dim)*lb;
end
%The initial radius of gragonflies' neighbourhoods
r=(ub-lb)/10;
Delta_max=(ub-lb)/10;
Food_fitness=inf;
Food_pos=zeros(dim,1);
Enemy_fitness=-inf;
Enemy_pos=zeros(dim,1);
X=initialization(SearchAgents_no,dim,ub,lb);
Fitness=zeros(1,SearchAgents_no);
DeltaX=initialization(SearchAgents_no,dim,ub,lb);
for iter=1:Max_iteration
r=(ub-lb)/4+((ub-lb)*(iter/Max_iteration)*2);
w=0.9-iter*((0.9-0.4)/Max_iteration);
my_c=0.1-iter*((0.1-0)/(Max_iteration/2));
if my_c<0
my_c=0;
end
s=2*rand*my_c; % Seperation weight
a=2*rand*my_c; % Alignment weight
c=2*rand*my_c; % Cohesion weight
f=2*rand; % Food attraction weight
e=my_c; % Enemy distraction weight
for i=1:SearchAgents_no %Calculate all the objective values first
Fitness(1,i)=fobj(X(:,i)');
All_fitness(1,i)=Fitness(1,i);
if Fitness(1,i)<Food_fitness
Food_fitness=Fitness(1,i);
Food_pos=X(:,i);
end
if Fitness(1,i)>Enemy_fitness
if all(X(:,i)<ub') && all( X(:,i)>lb')
Enemy_fitness=Fitness(1,i);
Enemy_pos=X(:,i);
end
end
end
for i=1:SearchAgents_no
index=0;
neighbours_no=0;
clear Neighbours_V
clear Neighbours_DeltaX
%find the neighbouring solutions
for j=1:SearchAgents_no
Dist2Enemy=distance(X(:,i),X(:,j));
if (all(Dist2Enemy<=r) && all(Dist2Enemy~=0))
index=index+1;
neighbours_no=neighbours_no+1;
Neighbours_DeltaX(:,index)=DeltaX(:,j);
Neighbours_X(:,index)=X(:,j);
end
end
% Seperation%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Eq. (3.1)
S=zeros(dim,1);
if neighbours_no>1
for k=1:neighbours_no
S=S+(Neighbours_X(:,k)-X(:,i));
end
S=-S;
else
S=zeros(dim,1);
end
% Alignment%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Eq. (3.2)
if neighbours_no>1
A=(sum(Neighbours_DeltaX')')/neighbours_no;
else
A=DeltaX(:,i);
end
% Cohesion%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Eq. (3.3)
if neighbours_no>1
C_temp=(sum(Neighbours_X')')/neighbours_no;
else
C_temp=X(:,i);
end
C=C_temp-X(:,i);
% Attraction to food%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Eq. (3.4)
Dist2Food=distance(X(:,i),Food_pos(:,1));
if all(Dist2Food<=r)
F=Food_pos-X(:,i);
else
F=0;
end
% Distraction from enemy%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Eq. (3.5)
Dist2Enemy=distance(X(:,i),Enemy_pos(:,1));
if all(Dist2Enemy<=r)
Enemy=Enemy_pos+X(:,i);
else
Enemy=zeros(dim,1);
end
for tt=1:dim
if X(tt,i)>ub(tt)
X(tt,i)=lb(tt);
DeltaX(tt,i)=rand;
end
if X(tt,i)<lb(tt)
X(tt,i)=ub(tt);
DeltaX(tt,i)=rand;
end
end
if any(Dist2Food>r)
if neighbours_no>1
for j=1:dim
DeltaX(j,i)=w*DeltaX(j,i)+rand*A(j,1)+rand*C(j,1)+rand*S(j,1);
if DeltaX(j,i)>Delta_max(j)
DeltaX(j,i)=Delta_max(j);
end
if DeltaX(j,i)<-Delta_max(j)
DeltaX(j,i)=-Delta_max(j);
end
X(j,i)=X(j,i)+DeltaX(j,i);
end
else
% Eq. (3.8)
X(:,i)=X(:,i)+Levy(dim)'.*X(:,i);
DeltaX(:,i)=0;
end
else
for j=1:dim
% Eq. (3.6)
DeltaX(j,i)=(a*A(j,1)+c*C(j,1)+s*S(j,1)+f*F(j,1)+e*Enemy(j,1)) + w*DeltaX(j,i);
if DeltaX(j,i)>Delta_max(j)
DeltaX(j,i)=Delta_max(j);
end
if DeltaX(j,i)<-Delta_max(j)
DeltaX(j,i)=-Delta_max(j);
end
X(j,i)=X(j,i)+DeltaX(j,i);
end
end
Flag4ub=X(:,i)>ub';
Flag4lb=X(:,i)<lb';
X(:,i)=(X(:,i).*(~(Flag4ub+Flag4lb)))+ub'.*Flag4ub+lb'.*Flag4lb;
end
Best_score=Food_fitness;
Best_pos=Food_pos;
cg_curve(iter)=Best_score;
if iter>2
line([iter-1 iter], [cg_curve(iter-1) cg_curve(iter)],'Color',[ 0.8500 0.3250 0.0980])
xlabel('Iteration');
ylabel('Best score obtained so far');
drawnow
end
results = get(handles.uitable1,'data');
results{4,1}=iter;
results{4,2}=Food_fitness;
set(handles.uitable1,'data',results);
end
function Positions=initialization(SearchAgents_no,dim,ub,lb)
Boundary_no= size(ub,2); % numnber of boundaries
% If the boundaries of all variables are equal and user enter a signle
% number for both ub and lb
if Boundary_no==1
ub_new=ones(1,dim)*ub;
lb_new=ones(1,dim)*lb;
else
ub_new=ub;
lb_new=lb;
end
% If each variable has a different lb and ub
for i=1:dim
ub_i=ub_new(i);
lb_i=lb_new(i);
Positions(:,i)=rand(SearchAgents_no,1).*(ub_i-lb_i)+lb_i;
end
Positions=Positions';