%%%%%%%%
pso_inertial.m %%%%%%%%
%%%%%Particle Swarm
Adjustment%%%%%%%%%%
Optimization
with
Inertial
Weight
%In particle Swarm Optimization the inertial weight which
decides the
%weightage of previous
later in the improved
velocity
value
was
introduced
%version of PSO.
%The inertial weightage value is taken to be linearly
decreasing and in
%some cases may even provide better and faster optimizing
results
%Look at the example below
%% Double Boundary Control Strategy
%It is used to have some variation in the population
which has violated the
%boundary consitions
%Method I
% If it violates it becomes equal to boundary value
%Method II
% If violated the boundary
upper and lower bound
condition
a
value
between
% is chosen
%% Example
% Type this in your command window :% low = [-10;-10;-10];
% up = low+20;
% pso_inertial('rosenbrock',40,3,low,up,9,1,10e+6);
% Whenever you feel the minimum value has been attained
Just Press ---> Ctrl+C
% The globalminimum value and globalminimizer will be
there in your workspace
% PSO_inertial can also be checked
benchmark functions - Ackley, Rastrigin
for
different
% Grienwank and FoxHoles. Only in the case of FoxHoles
change dim = 2
%% code for PSO
function pso_inertial(fnc,popsize,dim,low,up,wf,wi,epoch)
%% Initialization and Poulation Generation
pop=GeneratePopulation(popsize,dim,low,up);
fitnesspop=feval(fnc,pop);
PBest=pop;
PBest_value=fitnesspop;
[GBest_value,avain]=min(fitnesspop);
for k=1:popsize
GBest(k,:)=pop(avain,:);
end
v=zeros(popsize,dim);
c1=1;
c2=2;
for i=1:epoch
w = wi+((wi-wf)*i)/epoch;
mp=1;
% working on hybridization of PSO hence mp
variable has been used
vel
=
w.*v
+
pop)+(c2*rand).*(GBest-pop));
offspring = vel + pop;
mp.*((c1*rand).*(PBest-
% BOUNDARY CONTROL IS ALWAYS NECESSARY
offspring=BoundaryControl(offspring,low,up);
% Cost Value of offspring
fitness_offspring=feval(fnc,offspring);
% updating the value of PBest and GBest
ind =
fitness_offspring<PBest_value;
PBest(ind,:)=offspring(ind,:);
PBest_value(ind,:)=fitness_offspring(ind,:);
[pop_best,ind1]=min(fitness_offspring);
if pop_best<GBest_value
for j=1:popsize
GBest(j,:)=offspring(ind1,:);
end
GBest_value=pop_best;
end
% the new co-ordinates and the velocity
pop = offspring;
v = vel;
assignin('base','globalminimizer',GBest(1,:));
assignin('base','globalminimum',GBest_value);
fprintf('PSO_byBhanu|%5.0f
%9.16f\n',i,GBest_value);
end
return
----->
%$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
$$$$$$$$$$
%%%%%%%
pso_nb.m
%%%%%%%%%%%
%%%%%Particle Swarm Optimization%%%%%%%%%%
%%It is the most basic code of PSO
%hence makes it easy to apply on atleast unconstrained
problems
%% Double Boundary Control Strategy
%It is used to have some variation in the population
which has violated the
%boundary consitions
%Method I
% If it violates it becomes equal to boundary value
%Method II
% If violated the boundary
upper and lower bound
condition
a
value
between
% is chosen
%% It is best for Newbies
% The code is vectorized and Hence makes it even faster.
% Syntax pso_nb('filename',popsize,dim,low,up,epoch)
% popsize - It is the number of swarms or basically the
total population
% In swarm intelligence a population of maximum 80 works
for every problem
% dim - It refers to the number of variables or the
dimensions of the search space
% low - A column matrix depicting the lower boundary for
each variable
% up - upper boundary level
% epoch - The max number of iterations.
%% Example
% Type this in your command window :% low = [-10;-10;-10];
% up = low+20;
% pso_nb('rosenbrock',40,3,low,up,10e+6);
% Whenever you feel the minimum value has been attained
Just Press ---> Ctrl+C
% The globalminimum value and globalminimizer will be
there in your workspace
% PSO can also be checked
functions - Ackley, Rastrigin
for
different
benchmark
% Grienwank and FoxHoles. Only in the case of FoxHoles
change dim = 2
%% code for PSO
function pso_nb(fnc,popsize,dim,low,up,epoch)
%% Initialization and Poulation Generation
pop=GeneratePopulation(popsize,dim,low,up);
fitnesspop=feval(fnc,pop);
PBest=pop;
PBest_value=fitnesspop;
[GBest_value,avain]=min(fitnesspop);
for k=1:popsize
GBest(k,:)=pop(avain,:);
end
v=zeros(popsize,dim);
c1=1;
c2=2;
w=2;
for i=1:epoch
mp=1;
% working on hybridization of PSO hence mp
variable has been used
vel
=
w.*v
+
pop)+(c2*rand).*(GBest-pop));
mp.*((c1*rand).*(PBest-
offspring = vel + pop;
% BOUNDARY CONTROL IS ALWAYS NECESSARY
offspring=BoundaryControl(offspring,low,up);
% Cost Value of offspring
fitness_offspring=feval(fnc,offspring);
% updating the value of PBest and GBest
ind =
fitness_offspring<PBest_value;
PBest(ind,:)=offspring(ind,:);
PBest_value(ind,:)=fitness_offspring(ind,:);
[pop_best,ind1]=min(fitness_offspring);
if pop_best<GBest_value
for j=1:popsize
GBest(j,:)=offspring(ind1,:);
end
GBest_value=pop_best;
end
% the new co-ordinates and the velocity
pop = offspring;
v = vel;
assignin('base','globalminimizer',GBest(1,:));
assignin('base','globalminimum',GBest_value);
fprintf('PSO_byBhanu|%5.0f
%9.16f\n',i,GBest_value);
end
return
----->
% $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
%%%%%%%%%%
%%to
GeneratePopulation.m
%%%%%%%%%%%
generate the population
function pop=GeneratePopulation(popsize,dim,low,up)
pop=ones(popsize,dim);
for i=1:popsize
for j=1:dim
pop(i,j)=rand*(up(j)-low(j))+low(j);
end
end
% for i=1:popsize
%
ch1=round(pop(i,26));
%
ch2=round(pop(i,25));
%
if (ch1-ch2)<3
%
if ch2<22
%
pop(i,26)=ch2+3+rand*(19-ch2);
%
end
%
if ch2>21
%
pop(i,25)=21*rand;
%
pop(i,26)=round(pop(i,25))+3;
%
end
%
end
%
assignin('base','firstpop',pop);
% end
end
%$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
$$$$$$$$$
%%%%%%%%
BoundaryControl.m %%%%%%%
function pop=BoundaryControl(pop,low,up)
%% Double boundary control strategy
% Strategy can be changed accordingly
[popsize,dim]=size(pop);
for i=1:popsize
for j=1:dim
k=rand<rand;
if pop(i,j)<low(j), if k, pop(i,j)=low(j); else
pop(i,j)=rand*(up(j)-low(j))+low(j); end, end
if pop(i,j)>up(j),
if k, pop(i,j)=up(j);
pop(i,j)=rand*(up(j)-low(j))+low(j); end, end
end
end
return
else