pso(1).m

pso算法源程序 介绍基本粒子群算法的MATLAB实现程序

%%####################################################################
%%#### Particle swarm optimization
%%#### With linkage operator
%%#### Deepak devicharan july 2003
%%####################################################################

%%## 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)