How can I use parallel loop in MATLAB CPSO - performance

Question

I want to implement parallel Clustering Particle Swarm Optimisation (CPSO) MATLAB using parfor loop but it is taking more elapsed time than using simple for loops.
So please help me execute parallel loop iterations in my code.
I have MATLAB R2013a and Intel core i3 processor.
matlabpool('open', 2)
tic;
for iteration=1:iterations
%CALCULATE EUCLIDEAN DISTANCES TO ALL CENTROIDS
distances=zeros(dataset_size(1),centroids,particles);
for particle=1:particles
for centroid=1:centroids
distance=zeros(dataset_size(1),1);
for data_vector=1:dataset_size(1)
%meas(data_vector,:)
distance(data_vector,1)=norm(swarm_pos(centroid,:,particle)-meas(data_vector,:));
end
distances(:,centroid,particle)=distance;
end
end
%fprintf('\nEnd, meas(dv) is %5.4f\n',meas(data_vector,:));
%ASSIGN MEASURES with CLUSTERS
for particle=1:particles
[value, index] = min(distances(:,:,particle),[],2);
c(:,particle) = index;
end
% PLOT STUFF... CLEAR HANDLERS
delete(pc); delete(txt);
pc = []; txt = [];
% PLOT STUFF...
hold on;
for particle=1:particles
for centroid=1:centroids
if any(c(:,particle) == centroid)
if dimensions == 3
pc = [pc plot3(swarm_pos(centroid,1,particle),swarm_pos(centroid,2,particle),swarm_pos(centroid,3,particle),'*','color',cluster_colors_vector(particle,:))];
elseif dimensions == 2
pc = [pc plot(swarm_pos(centroid,1,particle),swarm_pos(centroid,2,particle),'*','color',cluster_colors_vector(particle,:))];
end
end
end
end
set(pc,{'MarkerSize'},{12})
hold off;
%CALCULATE GLOBAL FITNESS and LOCAL FITNESS:=swarm_fitness
average_fitness = zeros(particles,1);
for particle=1:particles
for centroid = 1 : centroids
if any(c(:,particle) == centroid)
local_fitness=mean(distances(c(:,particle)==centroid,centroid,particle));
average_fitness(particle,1) = average_fitness(particle,1) + local_fitness;
end
end
average_fitness(particle,1) = average_fitness(particle,1) / centroids;
if (average_fitness(particle,1) < swarm_fitness(particle))
swarm_fitness(particle) = average_fitness(particle,1);
swarm_best(:,:,particle) = swarm_pos(:,:,particle); %LOCAL BEST FITNESS
end
end
[global_fitness, index] = min(swarm_fitness); %GLOBAL BEST FITNESS
swarm_overall_pose = swarm_pos(:,:,index); %GLOBAL BEST POSITION
fprintf('global position is %5.4f\n',swarm_overall_pose);
% SOME INFO ON THE COMMAND WINDOW
fprintf('%3d. global fitness is %5.4f\n',iteration,global_fitness);
%uicontrol('Style','text','Position',[40 20 180 20],'String',sprintf('Actual fitness is: %5.4f', global_fitness),'BackgroundColor',get(gcf,'Color'));
pause(simtime);
% SAMPLE r1 AND r2 FROM UNIFORM DISTRIBUTION [0..1]
r1 = rand;
r2 = rand;
% UPDATE CLUSTER CENTROIDS
%p = Par(50);
for particle=1:particles
inertia = w * swarm_vel(:,:,particle);
cognitive = c1 * r1 * (swarm_best(:,:,particle)-swarm_pos(:,:,particle));
social = c2 * r2 * (swarm_overall_pose-swarm_pos(:,:,particle));
vel = inertia+cognitive+social;
swarm_pos(:,:,particle) = swarm_pos(:,:,particle) + vel ; % UPDATE PARTICLE POSE
swarm_vel(:,:,particle) = vel; % UPDATE PARTICLE VEL
end
%stop(p);
%plot(p);
% telapsed = toc(tstart);
% minTime = min(telapsed,minTime);
end