pso1.m

PSO粒子群寻优算法。 Particle swarm optimization With linkage operator

%%####################################################################
%%#### Particle swarm optimization
%%#### With linkage operator
%%#### Duan Dengxin 2006-5-16
%%####################################################################


%初始化各项参数

max_iterations  = 1000;
no_of_particles = 50;
dimensions      = 1;

%delta_min       = -0.003;
%delta_max       = 0.003;

c1 = 2;
c2 = 2;

%u = [0.01:0.0001:1];
%v = 5 + sin(pi*u) + 0.3*sign(u-0.3)... % 采用"HeaviSine信号"
%    + 0.8*sign(0.5-u) + 0.1*sign(u-0.2)...
%    + 0.2*sign(u-0.1) + 0.1*sign(-u+0.5)...
%    + 0.7*sign(-u+0.6) + 0.1*sign(-u+0.7);
%ddx = awgn(v,30); %在信号中加入信噪比为80dB的白高斯噪声
%plot(x,y);
%plot(x,z);  
%[CA,CD] = DWT(ddx,'db4');
%gcv(CA,0.01)
%plot(ddb);title('test');
%plot(x,ddx);
%[CA,CH,CV,CD] = dwt2(ddx,'db1');

%加载信号

load signal;

%初始化微粒位置和速度参数

for count_x = 1:no_of_particles
    for count_y = 1:dimensions
        particle_position(count_x,count_y) = rand;
        particle_velocity(count_x,count_y) = rand/20;
        p_best(count_x,count_y) = particle_position(count_x,count_y);
    end
end

%初始化pbest

for count = 1:no_of_particles
    p_best_fitness(count) = -1000;
end


%主要程序，进行PSO运算

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);
%====================


%plot(u,ddx);
current_fitness(count_x) = gcv(CD,x); % fitness值即为gcv值
%current_fitness(count_x) = 5-(x-1)^2;
%====================
            
        
%    soln = x-7;
        
%        if soln~=0 
%            current_fitness(count_x) = 1/abs(soln);
%        else
%            current_fitness =1000;
%       end
    end
    
    %确定pbest
    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
        
    %确定gbest
    [g_best_val,g_best_index] = max(current_fitness);
    
    for count_y = 1:dimensions
        g_best(count_y) = particle_position(g_best_index,count_y);      
    end

    %微粒位置和速度改变
    
    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

%给出结果

g_best        
current_fitness(g_best_index)