%%####################################################################
%%#### Particle swarm optimization
%%#### With linkage operator
%%#### Deepak devicharan july 2003
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%%## to apply this to different equations do the following
%%## generate initial particles in a search space close to actual soln
%%## fool around with no of iterations, no of particles, learning rates
%%## for a truly generic PSO do the following
%%## increase the number of particles , increase the variance
%%## i.e let the particles cover a larger area of the search space
%%## then fool around as always with the above thins
%declare the parameters of the optimization
max_iterations = 1000;
no_of_particles = 50;
dimensions = 1;
delta_min = -0.003;
delta_max = 0.003;
c1 = 1.3;
c2 = 1.3;
%initialise the particles and teir velocity components
for count_x = 1:no_of_particles
for count_y = 1:dimensions
particle_position(count_x,count_y) = rand*10;
particle_velocity(count_x,count_y) = rand;
p_best(count_x,count_y) = particle_position(count_x,count_y);
end
end
%initialize the p_best_fitness array
for count = 1:no_of_particles
p_best_fitness(count) = -1000;
end
%particle_position
%particle_velocity
%main particle swrm routine
for count = 1:max_iterations
%find the fitness of each particle
%change fitness function as per equation requiresd and dimensions
for count_x = 1:no_of_particles
%x = particle_position(count_x,1);
%y = particle_position(count_x,2);
%z = particle_position(count_x,3);
%soln = x^2 - 3*y*x + z;
%x = particle_position(count_x);
%soln = x^2-2*x+1;
x = particle_position(count_x);
soln = x-7;
if soln~=0
current_fitness(count_x) = 1/abs(soln);
else
current_fitness =1000;
end
end
%decide on p_best etc for each particle
for count_x = 1:no_of_particles
if current_fitness(count_x) > p_best_fitness(count_x)
p_best_fitness(count_x) = current_fitness(count_x);
for count_y = 1:dimensions
p_best(count_x,count_y) = particle_position(count_x,count_y);
end
end
end
%decide on the global best among all the particles
[g_best_val,g_best_index] = max(current_fitness);
%g_best contains the position of teh global best
for count_y = 1:dimensions
g_best(count_y) = particle_position(g_best_index,count_y);
end
%update the position and velocity compponents
for count_x = 1:no_of_particles
for count_y = 1:dimensions
p_current(count_y) = particle_position(count_x,count_y);
end
for count_y = 1:dimensions
particle_velocity(count_y) = particle_velocity(count_y) + c1*rand*(p_best(count_y)-p_current(count_y)) + c2*rand*(g_best(count_y)-p_current(count_y));
particle_positon(count_x,count_y) = p_current(count_y) +particle_velocity(count_y);
end
end
end
g_best
current_fitness(g_best_index)
clear all, clc % pso example
iter = 1000; % number of algorithm iterations
np = 2; % number of model parameters
ns = 10; % number of sets of model parameters
Wmax = 0.9; % maximum inertial weight
Wmin = 0.4; % minimum inertial weight
c1 = 2.0; % parameter in PSO methodology
c2 = 2.0; % parameter in PSO methodology
Pmax = [10 10]; % maximum model parameter value
Pmin = [-10 -10]; % minimum model parameter value
Vmax = [1 1]; % maximum change in model parameter
Vmin = [-1 -1]; % minimum change in model parameter
modelparameters(1:np,1:ns) = 0; % set all model parameter estimates for all model parameter sets to zero
modelparameterchanges(1:np,1:ns) = 0; % set all change in model parameter estimates for all model parameter sets to zero
bestmodelparameters(1:np,1:ns) = 0; % set best model parameter estimates for all model parameter sets to zero
setbestcostfunction(1:ns) = 1e6; % set best cost function of each model parameter set to a large number
globalbestparameters(1:np) = 0; % set best model parameter values for all model parameter sets to zero
bestparameters = globalbestparameters'; % b�
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