Changeset 36 for liacs/nc

Timestamp:

Dec 16, 2009, 4:21:58 PM (15 years ago)

Author:

Rick van der Zwet

Message:

Working version for octave, ajusted to match MatLab code

File:

• liacs/nc/laser-pulse-shaping/pso.m (modified) (5 diffs)

liacs/nc/laser-pulse-shaping/pso.m

@@ -4 +4 @@
 % Modeled after http://en.wikipedia.org/wiki/Particle_swarm_optimization
 
-% Parameters
-iterations = 1;
-parameters = 3;
-local_swarm_size = 2;
-local_swarms = 5;
-wander = 0.5;
+% Dimention settings
+parameters = 80;
+
+% If global optimum does not change this many steps bail out 
+iteration_break = 5;
+max_iterations = 1000;
+max_time = 5 * 60; % in sec
+
+% Flock properties 
+local_swarm_size = 10;
+local_swarms = 50;
+
+%% Particle properties
+% Speed of walking around to a certain direction
+wander = 0.4;
 
 % 'Influence' of the envirionment with regards to solutions
@@ -18 +27 @@
 % Trust the own best solution to be feasible
 c_ego = 0.2;
+
+% Variables used for plotting
+fitness_history = [];
+fitness_iterations = [];
 
 % Initiate all particles
@@ -29 +42 @@
 % at (:,x) lives the neighbor best of local_swarm 'x'
 n_best = zeros(parameters,local_swarms);
-n_fitness = ones(local_swarms);
+n_fitness = ones(parameters,local_swarms);
 % at (:,p,x) leves the local best of particle 'p' in local_swarm 'x'
 l_best = zeros(parameters,local_swarm_size,local_swarms);
 l_fitness = ones(local_swarm_size, local_swarms);
 
+idle_counter = 0;
+start_time = time();
 
 % Code not optimised for performance, but for readablility
-for i = 1:iterations
+for i = 1:max_iterations
     for s = 1:local_swarms
         fitness = SHGa(flock_p(:,:,s));
@@ -56 +71 @@
     end
 
+    idle_counter = idle_counter + 1;
+
     % See wether we have a new global optimum
     for s = 1:local_swarms
         if n_fitness(s) < g_fitness
           g_fitness = n_fitness(s);
-          g_best = n_best(s);
+          g_best = n_best(:,s);
+          idle_counter = 0;
         end
     end
+
+    % Stop conditions
+    if idle_counter == iteration_break 
+        printf("Caught by idle_counter\n");
+        break;
+    end
+    if time - start_time > max_time
+        printf("Caught by max_time used \n");
+        break;
+    end
+    
+
+    printf("%i : %.15f\n", i, g_fitness);
+    fitness_iterations = [fitness_iterations, i];
+    fitness_history = [fitness_history, g_fitness];
+    
 
     % Update particles to new value
@@ -71 +105 @@
         for p = 1:local_swarm_size
             flock_v(:,p,s) = flock_v(:,p,s) * wander;
-            flock_v(:,p,s) =+ g_best * (c_cognitive * r_cognitive);
-            flock_v(:,p,s) =+ n_best(s) * (c_social * r_social);
-            flock_v(:,p,s) =+ l_best(p,s) .* (c_ego *r_ego);
-            flock_p(:,p,s) =+ flock_v(:,p,s);
+            flock_v(:,p,s) = flock_v(:,p,s) + (g_best - flock_p(:,p,s)) * (c_cognitive * r_cognitive);
+            flock_v(:,p,s) = flock_v(:,p,s) + (n_best(:,s) - flock_p(:,p,s)) * (c_social * r_social);
+            flock_v(:,p,s) = flock_v(:,p,s) + (l_best(:,p,s) - flock_p(:,p,s)) * (c_ego * r_ego);
+            flock_p(:,p,s) = flock_p(:,p,s) + flock_v(:,p,s);
         end
     end
 end
-i = 1:10;
-y = i;
-plot(i,y);
-print -deps pso-fitness.eps;
-%title(sprintf('Particle Swarm Optimalisation on Laser-Pulse shaping problem - repetitions %i',repetitions));
-%ylabel('fitness');
-%xlabel('iterations');
-%grid on;
-%legend(sprintf('Length %i',length));
-%print("sa-fitness.eps","-depsc2");
 
+
+plot(fitness_iterations,fitness_history);
+title(sprintf('Particle Swarm Optimalisation on Laser-Pulse shaping problem'));
+ylabel('fitness');
+xlabel('iterations');
+grid on;
+legend(sprintf('Parameters %i',parameters));
+print("pso-fitness.eps","-depsc2");
+