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Particle Swarm Optimization (PSO)

version 1.0.0.0 (5.25 KB) by Yarpiz
A simple structured MATLAB implementation of PSO

127 Downloads

Updated 04 Sep 2015

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For more information, see the following link:
http://yarpiz.com/50/ypea102-particle-swarm-optimization

Cite As

Yarpiz (2020). Particle Swarm Optimization (PSO) (https://www.mathworks.com/matlabcentral/fileexchange/52857-particle-swarm-optimization-pso), MATLAB Central File Exchange. Retrieved .

Comments and Ratings (18)

hongyu

shuai liu

Revanth Ram

Hello sir I want to minimise cos5x1+cos5x2+cos5x3+cos5x4=0
Cos7x1+cos7x2+cos7x3+cos7x4 =0
Cos11x1+cos11x2+cos11x3+cos11x4 =0
Where i have 4 unknown variables x1, x2, x3,x4 and condition is 0<x1<x2<x3<x4<pi/2.
Can you please help me about the code.
This is my engineering project. Thanking you sir..
Mail revanthroy507@gmail.com

Thank You.... And Coding is marvellous....Superb JOB..

Sir,
I need a some idea to develop a code for IIR filter design

I need pso program to eliminate harmonics in multilevel inverters for singlephase

vasu koneti

sir,
I tried to use your code for tunning the parameters of FOPID controller,iam getting error at objective function ,please help me sir.

hello can you help me in this code
i will transfer this code in programming
pso -->psprogramming
function [fxmin, xmin, Swarm, history] = PSO(psoOptions)

%Globals
global psoFlags;
global psoVars;
global psoSParameters;
global notifications;

upbnd = 600; % Upper bound for init. of the swarm
lwbnd = 300; % Lower bound for init. of the swarm
GM = 0; % Global minimum (used in the stopping criterion)
ErrGoal = 1e-10; % Desired accuracy
%
%Initializations
if nargin == 0
psoOptions = get_psoOptions;
end
%For Displaying
if psoOptions.Flags.ShowViz
global vizAxes; %Use the specified axes if using GUI or create a new global if called from command window
vizAxes = plot(0,0, '.');
axis([-1000 1000 -1000 1000 -1000 1000]); %Initially set to a cube of this size
axis square;
grid off;
set(vizAxes,'EraseMode','xor','MarkerSize',15); %Set it to show particles.
pause(1);
end
%End Display initialization

% Initializing variables
success = 0; % Success Flag
iter = 0; % Iterations' counter
fevals = 0; % Function evaluations' counter

% Using params---
% Determine the value of weight change
w_start = psoOptions.SParams.w_start; %Initial inertia weight's value
w_end = psoOptions.SParams.w_end; %Final inertia weight
w_varyfor = floor(psoOptions.SParams.w_varyfor*psoOptions.Vars.Iterations); %Weight change step. Defines total number of iterations for which weight is changed.
w_now = w_start;
inertdec = (w_start-w_end)/w_varyfor; %Inertia weight's change per iteration

% Initialize Swarm and Velocity
SwarmSize = psoOptions.Vars.SwarmSize;
Swarm = rand(SwarmSize, psoOptions.Vars.Dim)*(psoOptions.Obj.ub-psoOptions.Obj.lb) + psoOptions.Obj.lb;
VStep = rand(SwarmSize, psoOptions.Vars.Dim);

f2eval = psoOptions.Obj.f2eval; %The objective function to optimize.

%Find initial function values.
fSwarm = feval(f2eval, Swarm);
fevals = fevals + SwarmSize;

% Initializing the Best positions matrix and
% the corresponding function values
PBest = Swarm;
fPBest = fSwarm;

% Finding best particle in initial population
[fGBest, g] = min(fSwarm);
lastbpf = fGBest;
Best = Swarm(g,:); %Used to keep track of the Best particle ever
fBest = fGBest;
history = [0, fGBest];

if psoOptions.Flags.Neighbor
% Define social neighborhoods for all the particles
for i = 1:SwarmSize
lo = mod(i-psoOptions.SParam.Nhood:i+psoOptions.SParam.Nhood, SwarmSize);
nhood(i,:) = [lo];
end
nhood(find(nhood==0)) = SwarmSize; %Replace zeros with the index of last particle.
end

if psoOptions.Disp.Interval & (rem(iter, psoOptions.Disp.Interval) == 0)
disp(sprintf('Iterations\t\tfGBest\t\t\tfevals'));
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% THE PSO LOOP %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
while( (success == 0) & (iter <= psoOptions.Vars.Iterations) )
iter = iter+1;

% Update the value of the inertia weight w
if (iter<=w_varyfor) & (iter > 1)
w_now = w_now - inertdec; %Change inertia weight
end


%%%%%%%%%%%%%%%%%
% The PLAIN PSO %

% Set GBest
A = repmat(Swarm(g,:), SwarmSize, 1); %A = GBest. repmat(X, m, n) repeats the matrix X in m rows by n columns.
B = A; %B wil be nBest (best neighbor) matrix

% use neighborhood model
% circular neighborhood is used
if psoOptions.Flags.Neighbor
for i = 1:SwarmSize
[fNBest(i), nb(i)] = min(fSwarm( find(nhood(i)) ));
B(i, :) = Swarm(nb(i), :);
end
end

% Generate Random Numbers
R1 = rand(SwarmSize, psoOptions.Vars.Dim);
R2 = rand(SwarmSize, psoOptions.Vars.Dim);

%% Calculate Velocity
if ~psoOptions.Flags.Neighbor %Normal
VStep = w_now*VStep + psoOptions.SParams.c1*R1.*(PBest-Swarm) + psoOptions.SParams.c2*R2.*(A-Swarm);
else %With neighborhood
R3 = rand(SwarmSize, psoOptions.Vars.Dim); %random nos for neighborhood
VStep = w_now*VStep + psoOptions.SParams.c1*R1.*(PBest-Swarm) + psoOptionsSParams.c2*R2.*(A-Swarm) + psoOptionsSParams.c3*R3.*(B-Swarm);
end
%%

% Apply Vmax Operator for v > Vmax
changeRows = VStep > psoOptions.SParams.Vmax;
VStep(find(changeRows)) = psoOptions.SParams.Vmax;
% Apply Vmax Operator for v < -Vmax
changeRows = VStep < -psoOptions.SParams.Vmax;
VStep(find(changeRows)) = -psoOptions.SParams.Vmax;

%% ::UPDATE POSITIONS OF PARTICLES::
Swarm = Swarm + psoOptions.SParams.Chi * VStep; % Evaluate new Swarm

fSwarm = feval(f2eval, Swarm);
fevals = fevals + SwarmSize;

%% Updating the best position for each particle
changeRows = fSwarm < fPBest;
fPBest(find(changeRows)) = fSwarm(find(changeRows));
PBest(find(changeRows), :) = Swarm(find(changeRows), :);

lastbpart = PBest(g, :);
% Updating index g
[fGBest, g] = min(fPBest);

%% %Update Best. Only if fitness has improved.
if fGBest < lastbpf
[fBest, b] = min(fPBest);
Best = PBest(b,:);
end

%% OUTPUT%%
if psoOptions.Save.Interval & (rem(iter, psoOptions.Save.Interval) == 0)
history((size(history,1)+1), :) = [iter, fBest];
end
if psoOptions.Disp.Interval & (rem(iter, psoOptions.Disp.Interval) == 0)
disp(sprintf('%4d\t\t\t%.5g\t\t\t%5d', iter, fGBest, fevals));
end
if psoOptions.Flags.ShowViz
[fworst, worst] = max(fGBest);
DrawSwarm(Swarm, SwarmSize, iter, psoOptions.Vars.Dim, Swarm(g,:), vizAxes);
end
%% TERMINATION%%
if abs(fGBest-psoOptions.Obj.GM) <= psoOptions.Vars.ErrGoal %GBest
success = 1;
elseif abs(fBest-psoOptions.Obj.GM)<=psoOptions.Vars.ErrGoal %Best
success = 1
else
lastbpf = fGBest; %To be used to find Best
end


end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% END OF PSO LOOP %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

[fxmin, b] = min(fPBest);
xmin = PBest(b, :);

history = history(:,1);
%Comment below line to Return Swarm. Uncomment to return previous best positions.
%Swarm = PBest;
%Return PBest

Hello, how can i use this for image segmentation ?

Can you help me to find the reduction of side lobe of circular array antenna using pso algorithm
please help me
nalluseye@gmail.com

EB

I can't run this program. Error found in the costfunction. help me please

Very clear example of how to use Particle Swarm Optimization. I had it running on my 11-dimensional optimization problem in no time at all. Many thanks.

By moving the GlobalBest reference out of the main loop, it is possible to use a parfor loop.

Hello sir,
Can you help me to find the optimal placement and capacity of Distributed Generation by using PSO?
please help me
rejeki.tambun@yahoo.com

sherin c

Updates

1.0.0.0

Image Added

MATLAB Release Compatibility
Created with R2012b
Compatible with any release
Platform Compatibility
Windows macOS Linux
Acknowledgements

Inspired: particle swarm optimized Neural Network

YPEA102 Particle Swarm Optimization/PSO/