www.pudn.com > geneticbs2rv.rar > MPGA.M

% mpga         multi population genetic algorithm 
% 
% This script implements the Multi Population Genetic Algorithm. 
% A real-valued representation of the individuals is used. 
% 
 
% Author:     Andrew Chipperfield 
% History:    30-Mar-94     file created 
 
NVAR = 20;		% No. of decision variables (control steps) 
RANGE = [0;200];	% Bounds on decision variables 
 
% Set field descriptor 
   FieldD = rep(RANGE,[1,NVAR]); 
 
% Define GA Parameters 
   GGAP = .8;		% Generation gap, how many new individuals are created 
   XOVR =  1;		% Crossover rate 
   MUTR = 1/NVAR;	% Mutation rate depending on NVAR 
   MAXGEN = 1200;	% Maximum number of generations 
   TERMEXACT = 1e-4;    % Value for termination if minimum reached 
   INSR = .9;		% Insertion rate, how many of the offspring are inserted 
   SUBPOP = 8;		% Number of subpopulations 
   MIGR = 0.2;		% Migration rate between subpopulations 
   MIGGEN = 20;		% Number of generations between migration 
   NIND = 20;		% Number of individuals per subpopulation 
 
% Specify other routines as strings 
   SEL_F = 'sus';       % Name of selection function 
   XOV_F = 'recdis';    % Name of recombination function for individuals 
   MUT_F = 'mutbga';    % Name of mutation function 
   OBJ_F = 'objharv';   % Name of function for objective values 
 
 
% Get value of minimum, defined in objective function 
   GlobalMin = feval(OBJ_F,[],3); 
 
% Get title of objective function, defined in objective function 
   FigTitle = [feval(OBJ_F,[],2) '   (' int2str(SUBPOP) ':' int2str(MAXGEN) ') ']; 
 
% Clear Best  and storing matrix 
   % Initialise Matrix for storing best results 
      Best = NaN * ones(MAXGEN,3); 
      Best(:,3) = zeros(size(Best,1),1); 
   % Matrix for storing best individuals 
      IndAll = []; 
 
% Create real population 
   Chrom = crtrp(SUBPOP*NIND,FieldD); 
 
% reset count variables 
   gen = 0; 
 
% Calculate objective function for population 
   ObjV = feval(OBJ_F,Chrom); 
   % count number of objective function evaluations 
   Best(gen+1,3) = Best(gen+1,3) + NIND; 
 
% Generational loop 
   while gen < MAXGEN, 
 
   % Save the best and average objective values and the best individual 
      [Best(gen+1,1),ix] = min(ObjV); 
      Best(gen+1,2) = mean(ObjV); 
      IndAll = [IndAll; Chrom(ix,:)]; 
 
   % Fitness assignment to whole population 
      FitnV = ranking(ObjV,2 ,SUBPOP); 
             
   % Select individuals from population 
      SelCh = select(SEL_F, Chrom, FitnV, GGAP, SUBPOP); 
       
   % Recombine selected individuals 
      SelCh=recombin(XOV_F, SelCh, XOVR, SUBPOP); 
 
   % Mutate offspring 
      SelCh=mutate(MUT_F, SelCh, FieldD, [MUTR], SUBPOP); 
 
   % Calculate objective function for offsprings 
      ObjVOff = feval(OBJ_F,SelCh); 
      Best(gen+1,3) = Best(gen+1,3) + size(SelCh,1); 
 
   % Insert best offspring in population replacing worst parents 
      [Chrom, ObjV] = reins(Chrom, SelCh, SUBPOP, [1 INSR], ObjV, ObjVOff); 
 
      gen=gen+1; 
 
   % Plot some results, rename title of figure for graphic output 
      if ((rem(gen,20) == 1) | (rem(gen,MAXGEN) == 0)), 
         set(gcf,'Name',[FigTitle ' in ' int2str(gen)]); 
         resplot(Chrom(1:2:size(Chrom,1),:),... 
                 IndAll(max(1,gen-39):size(IndAll,1),:),... 
                 [ObjV; GlobalMin], Best(max(1,gen-19):gen,[1 2]), gen); 
      end 
 
 
   % migrate individuals between subpopulations 
      if (rem(gen,MIGGEN) == 0) 
         [Chrom, ObjV] = migrate(Chrom, SUBPOP, [MIGR, 1, 0], ObjV); 
      end 
 
   end 
 
 
% Results 
   % add number of objective function evaluations 
   Results = cumsum(Best(1:gen,3)); 
   % number of function evaluation, mean and best results 
   Results = [Results Best(1:gen,2) Best(1:gen,1)]; 
    
% Plot Results and show best individuals => optimum 
   figure('Name',['Results of ' FigTitle]); 
   subplot(2,1,1), plot(Results(:,1),Results(:,2),'-',Results(:,1),Results(:,3),':'); 
   subplot(2,1,2), plot(IndAll(gen-4:gen,:)'); 
  
 
% End of script