SPAN (张友邦)
注册： 2008-04-24
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%

% Author: Zhang Youbang

% Time: 2009-06-11

% Version: 1.0

%

% This is a basic PSO algorithm implementation.

%

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clear all

clear output

format compact

% some parameters

particle_number = 500; % total number of particles

iteration_number = 200; % the maximum iteration number

wmax = 20;

wmin = 0.01;

c1 = 2;

c2 = 2;

velocity_max = [15, 15];

velocity_init = [0, 0];

% initliaze particles

gbest_index = 1; % the global best particle's index

for progress = 1:iteration_number

  w(progress) = wmax - ((wmax - wmin) / iteration_number) * progress;

end

for progress = 1:iteration_number

  for i = 1:particle_number

    if progress == 1 % initialize the particle

      particles(i).location = [(rand - 0.5) * 10000, (rand - 0.5) * 10000];

      particles(i).fitness_value = pso_fittness_function(particles(i).location);

      

      particles(i).pbest_location = particles(i).location;

      particles(i).pbest_fitness_value = particles(i).fitness_value;

      particles(i).velocity = velocity_init; % just set it to zero

    elseif i ~= gbest_index % fly towards the global best fitted particle

      particles(i).velocity = w(progress) * particles(i).velocity + c1 * rand() * (particles(i).pbest_location - particles(i).location) + c2 * rand() * (particles(gbest_index).location - particles(i).location);

      

      % constraint velocity

      if particles(i).velocity(1) > velocity_max(1);particles(i).velocity(1) = velocity_max(1);end;

      if particles(i).velocity(1) < -velocity_max(1);particles(i).velocity(1) = -velocity_max(1);end;

      if particles(i).velocity(2) > velocity_max(2);particles(i).velocity(2) = velocity_max(2);end;

      if particles(i).velocity(2) < -velocity_max(2);particles(i).velocity(2) = -velocity_max(2);end;

      

      particles(i).location = particles(i).location + particles(i).velocity;

      particles(i).fitness_value = pso_fittness_function(particles(i).location);

    end

    % update local best location

    if particles(i).fitness_value < particles(i).pbest_fitness_value

      particles(i).pbest_location = particles(i).location;

      particles(i).pbest_fitness_value = particles(i).fitness_value;

    end

    % update global best fitted location

    if particles(i).pbest_fitness_value < particles(gbest_index).pbest_fitness_value

      gbest_index = i;

    end

  end % end of i

  

  % show the calculation progress

  [progress, particles(gbest_index).pbest_fitness_value]

end % end of n

% print the results

particles(gbest_index).pbest_location

particles(gbest_index).pbest_fitness_value

% end of this file