Particle Swarm Optimization

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07 Jun 2008 (Updated )

Particle swarm optimization animation

PSO.m
%% Particle Swarm Optimization Simulation
% Animiation of birds movement of a swarm to get the global minimum solution 
%
% Author: Wael Mansour (wael192@yahoo.com)
%
% MSc Student, Electrical Enginering Dept, 
% Faculty of Engineering Cairo University, Egypt

%% Initialization
clear
clc
n = 50;          % Size of the swarm " no of birds "
bird_setp  = 50; % Maximum number of "birds steps"
dim = 2;          % Dimension of the problem

c2 =1.2;          % PSO parameter C1 
c1 = 0.12;        % PSO parameter C2 
w =0.9;           % pso momentum or inertia  
fitness=0*ones(n,bird_setp);

                                       %-----------------------------%
                                       %    initialize the parameter %
                                       %-----------------------------%
                                       
R1 = rand(dim, n);
R2 = rand(dim, n);
current_fitness =0*ones(n,1);

                                 %------------------------------------------------%
                                 % Initializing swarm and velocities and position %
                                 %------------------------------------------------%
                                 
current_position = 10*(rand(dim, n)-.5);
velocity = .3*randn(dim, n) ;
local_best_position  = current_position ;


                                 %-------------------------------------------%
                                 %     Evaluate initial population           %           
                                 %-------------------------------------------%

for i = 1:n
    current_fitness(i) = Live_fn(current_position(:,i));    
end


local_best_fitness  = current_fitness ;
[global_best_fitness,g] = min(local_best_fitness) ;

for i=1:n
    globl_best_position(:,i) = local_best_position(:,g) ;
end
                                               %-------------------%
                                               %  VELOCITY UPDATE  %
                                               %-------------------%

velocity = w *velocity + c1*(R1.*(local_best_position-current_position)) + c2*(R2.*(globl_best_position-current_position));

                                               %------------------%
                                               %   SWARMUPDATE    %
                                               %------------------%
                                               
            
current_position = current_position + velocity ;

                                               %------------------------%
                                               %  evaluate anew swarm   %
                                               %------------------------%
                                               

%% Main Loop
iter = 0 ;        % Iterationscounter
while  ( iter < bird_setp )
iter = iter + 1;

for i = 1:n,
current_fitness(i) = Live_fn(current_position(:,i)) ;    

end


for i = 1 : n
        if current_fitness(i) < local_best_fitness(i)
           local_best_fitness(i)  = current_fitness(i);  
           local_best_position(:,i) = current_position(:,i)   ;
        end   
 end

  
 [current_global_best_fitness,g] = min(local_best_fitness);
  
    
if current_global_best_fitness < global_best_fitness
   global_best_fitness = current_global_best_fitness;
   
    for i=1:n
        globl_best_position(:,i) = local_best_position(:,g);
    end
   
end


 velocity = w *velocity + c1*(R1.*(local_best_position-current_position)) + c2*(R2.*(globl_best_position-current_position));
 current_position = current_position + velocity; 
  
 

 
x=current_position(1,:);
y=current_position(2,:);

clf    
    plot(x, y , 'h')   
    axis([-5 5 -5 5]);
    
pause(.2)


end % end of while loop its mean the end of all step that the birds move it 
                      

              [Jbest_min,I] = min(current_fitness) % minimum fitness
               current_position(:,I) % best solution

               


    

%          
         

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