epso.m

matlab code for exponential PSO

function out=epso(functchc,D,Maxgen,epochtime,Rmax,Rmin,Stop_criteria)
% Exponential Distribution PSO    
global initial_flag EpochtimeArr tr FE_success_runs success_runs M O
global result_type
% Parameter Settings
NP=25;C1=2;C2=2;Xmax(1:D)=Rmax;Xmin(1:D)=Rmin;
Vmax = 0.25*(Xmax - Xmin);

%------------------------------
%---------- Initialization of swarm -------------
for count_p = 1:NP
    for count_d= 1:D
        X(count_p,count_d) = edrand;
        V(count_p,count_d) = edrand;
        p_best(count_p,count_d) = X(count_p,count_d);
    end
end
%------------------------------
%-------------Evaluate fitness value pbest and gbest of all particles
for count_p = 1:NP
            for count_d= 1:D
               xy(count_d)=X(count_p,count_d);
            end
        current_fitness(count_p)=BENCH_FUNCS(xy,functchc);
end 
                      
    [g_best_val,g_best_index] = min(current_fitness);
    for count_d = 1:D
      g_best(count_d) = X(g_best_index,count_d);
    end
%-----------------------------------------------
if epochtime ==1
    tic;
end
%-------- Main routine starts ---------------
for count_g = 1:Maxgen
% ----------  Velocity and Possition Updation ---------------------------
           for count_p = 1:NP
                   for count_d= 1:D 
  V(count_p,count_d)=edrand*(p_best(count_p,count_d)-X(count_p,count_d))+edrand*(g_best(count_d)-X(count_p,count_d));
 V(count_p,count_d)=min(Vmax(count_d),max(-Vmax(count_d),V(count_p,count_d)));
                    X(count_p,count_d)=X(count_p,count_d)+V(count_p,count_d);
                  end
             end                    
%------------------------------------------------------------------------
             
 % -------- Computing f(xi) ---------
    for count_p = 1:NP    
        if( X(count_p,:) > Xmin & X(count_p,:) < Xmax)
                for count_d= 1:D
                     xy(count_d)=X(count_p,count_d);
                end
              current_fitness(count_p)=BENCH_FUNCS(xy,functchc);
        end   
    end
% -------- updating pbest---------    
    for count_p = 1:NP
        for count_d= 1:D
          xy(count_d)=p_best(count_p,count_d);
        end
       p_best_fitness(count_p)=BENCH_FUNCS(xy,functchc);        
        
            if current_fitness(count_p)< p_best_fitness(count_p)
                     p_best_fitness(count_p) = current_fitness(count_p);
                        for count_d = 1:D
                            p_best(count_p,count_d) = X(count_p,count_d);
                        end
           end
    end
%-----------------------------
%--------- Updating gbest------------
      for count_d= 1:D
          xy(count_d)=g_best(count_d);
      end
       g_best_fitness(count_g)=BENCH_FUNCS(xy,functchc);        
 
       [g_best_val,g_best_index] = min(p_best_fitness);
       if g_best_val < g_best_fitness(count_g)
           g_best_fitness(count_g)=g_best_val;
           for count_d = 1:D
                   g_best(count_d) = p_best(g_best_index,count_d);
           end        
       end
%---------------------------------------                     
            % -------- Stop criteria ---------
             if g_best_fitness(count_g) <Stop_criteria   
             disp('Stop criteria');
             success_runs= success_runs+1;
             FE_success_runs(success_runs)=g_best_fitness(count_g);
                break
             end
           %---------------------------------
                    
end
if epochtime ==1
    EpochtimeArr(tr)=toc;
end

for n=count_g:Maxgen
    g_best_fitness(n)=g_best_fitness(count_g);
end


Value=g_best;

if result_type==1
    out=g_best_fitness;
    FitnessEPSO=g_best_fitness(Maxgen)
else
    out=g_best_fitness(Maxgen);
end