pso2.m

许多的PSO优化算法

%% Particle Swarm Optimization Simulation
% Simulates the movements of a swarm to minimize the objective function
% 
% $$ \left( x-15 \right) ^{2}+ \left( y-20 \right) ^{2} = 0$$
% 
% The swarm matrix is  
%
% swarm(index, [location, velocity, best position, best
% value], [x, y components or the value component])
%
% Author: Wesam ELSHAMY ([e-mail])
% MSc Student, Electrical Enginering Dept., Faculty of Engineering Cairo University, Egypt
%%


%% Initialization
% Parameters
clear
clc
iterations = 30;
inertia = 1.0;
correction_factor = 2.0;
swarm_size = 49;

% ---- initial swarm position -----
index = 1;
for i = 1 : 7
    for j = 1 : 7
        swarm(index, 1, 1) = i;
        swarm(index, 1, 2) = j;
        index = index + 1;
    end
end

swarm(:, 4, 1) = 1000;          % best value so far
swarm(:, 2, :) = 0;             % initial velocity

%% Iterations
for iter = 1 : iterations
    
    %-- evaluating position & quality ---
    for i = 1 : swarm_size
        swarm(i, 1, 1) = swarm(i, 1, 1) + swarm(i, 2, 1)/1.3;     %update x position
        swarm(i, 1, 2) = swarm(i, 1, 2) + swarm(i, 2, 2)/1.3;     %update y position
        x = swarm(i, 1, 1);
        y = swarm(i, 1, 2);
        
        val = (x - 15)^2 + (y - 20)^2;          % fitness evaluation (you may replace this objective function with any function having a global minima)
        
        if val < swarm(i, 4, 1)                 % if new position is better
            swarm(i, 3, 1) = swarm(i, 1, 1);    % update best x,
            swarm(i, 3, 2) = swarm(i, 1, 2);    % best y postions
            swarm(i, 4, 1) = val;               % and best value
        end
    end

    [temp, gbest] = min(swarm(:, 4, 1));        % global best position
    
    %--- updating velocity vectors
    for i = 1 : swarm_size
        swarm(i, 2, 1) = rand*inertia*swarm(i, 2, 1) + correction_factor*rand*(swarm(i, 3, 1) - swarm(i, 1, 1)) + correction_factor*rand*(swarm(gbest, 3, 1) - swarm(i, 1, 1));   %x velocity component
        swarm(i, 2, 2) = rand*inertia*swarm(i, 2, 2) + correction_factor*rand*(swarm(i, 3, 2) - swarm(i, 1, 2)) + correction_factor*rand*(swarm(gbest, 3, 2) - swarm(i, 1, 2));   %y velocity component
    end
    
    %% Plotting the swarm
    clf    
    plot(swarm(:, 1, 1), swarm(:, 1, 2), 'x')   % drawing swarm movements
    axis([-2 30 -2 30]);
pause(.2)
end