【BP分类】基于头脑风暴算法优化BP神经网络实现数据分类附matlab代码

function best_fitness = bso2(fun,n_p,n_d,n_c,rang_l,rang_r,max_iteration)% fun = fitness_function% n_p; population size% n_d; number of dimension% n_c: number of clusters% rang_l; left boundary of the dynamic range% rang_r; right boundary of the dynamic rangeprob_one_cluster = 0.8;                                    % probability for select one cluster to form new individual; stepSize = ones(1,n_d);                                    % effecting the step size of generating new individuals by adding random valuespopu = rang_l + (rang_r - rang_l) * rand(n_p,n_d);         % initialize the population of individualspopu_sorted  = rang_l + (rang_r - rang_l) * rand(n_p,n_d); % initialize the  population of individuals sorted according to clustersn_iteration = 0;                                           % current iteration number% initialize cluster probability to be zerosprob = zeros(n_c,1);best = zeros(n_c,1);                                       % index of best individual in each clustercenters = rang_l + (rang_r - rang_l) * rand(n_c,n_d);      % initialize best individual in each clustercenters_copy = rang_l + (rang_r - rang_l) * rand(n_c,n_d); % initialize best individual-COPY in each cluster FOR the purpose of introduce random bestbest_fitness = 1000000*ones(max_iteration,1);fitness_popu = 1000000*ones(n_p,1);                        % store fitness value for each individualfitness_popu_sorted = 1000000*ones(n_p,1);                 % store  fitness value for each sorted individualindi_temp = zeros(1,n_d);                                  % store temperary individual% calculate fitness for each individual in the initialized populationfor idx = 1:n_p    fitness_popu(idx,1) = fun(popu(idx,:));endwhile n_iteration < max_iteration         cluster = kmeans(popu, n_c,'Distance','cityblock','Start',centers,'EmptyAction','singleton'); % k-mean cluster      % clustering          fit_values = 100000000000000000000000000.0*ones(n_c,1);  % assign a initial big fitness value  as best fitness for each cluster in minimization problems      number_in_cluster = zeros(n_c,1);                        % initialize 0 individual in each cluster      for idx = 1:n_p          number_in_cluster(cluster(idx,1),1)= number_in_cluster(cluster(idx,1),1) + 1;                % find the best individual in each cluster          if fit_values(cluster(idx,1),1) > fitness_popu(idx,1)  % minimization             fit_values(cluster(idx,1),1) = fitness_popu(idx,1);             best(cluster(idx,1),1) = idx;          end      end        % form population sorted according to clusters      counter_cluster = zeros(n_c,1);  % initialize cluster counter to be 0       acculate_num_cluster = zeros(n_c,1);  % initialize accumulated number of individuals in previous clusters      for idx =2:n_c          acculate_num_cluster(idx,1) = acculate_num_cluster((idx-1),1) + number_in_cluster((idx-1),1);      end      %start form sorted population      for idx = 1:n_p          counter_cluster(cluster(idx,1),1) = counter_cluster(cluster(idx,1),1) + 1 ;          temIdx = acculate_num_cluster(cluster(idx,1),1) +  counter_cluster(cluster(idx,1),1);          popu_sorted(temIdx,:) = popu(idx,:);          fitness_popu_sorted(temIdx,1) = fitness_popu(idx,1);      end        % record the best individual in each cluster      for idx = 1:n_c          centers(idx,:) = popu(best(idx,1),:);              end        if (rand() < 0.2) %  select one cluster center to be replaced by a randomly generated center         cenIdx = ceil(rand()*n_c);         centers(cenIdx,:) = rang_l + (rang_r - rang_l) * rand(1,n_d);      end               % calculate cluster probabilities based on number of individuals in each cluster      for idx = 1:n_c          prob(idx,1) = number_in_cluster(idx,1)/n_p;          if idx > 1             prob(idx,1) = prob(idx,1) + prob(idx-1,1);          end      end      % generate n_p new individuals by adding Gaussian random values                 for idx = 1:n_p          r_1 = rand();             % probability for select one cluster to form new individual          if r_1 < prob_one_cluster  % select one cluster             r = rand();             for idj = 1:n_c                 if r < prob(idj,1)                                          if rand() < 0.4  % use the center                       indi_temp(1,:) = centers(idj,:);                     else             % use one randomly selected  cluster                        indi_1 = acculate_num_cluster(idj,1) + ceil(rand() * number_in_cluster(idj,1));                        indi_temp(1,:) = popu_sorted(indi_1,:);                      end                    break                end             end          else % select two clusters               % pick two clusters                cluster_1 = ceil(rand() * n_c);               indi_1 = acculate_num_cluster(cluster_1,1) + ceil(rand() * number_in_cluster(cluster_1,1));               cluster_2 = ceil(rand() * n_c);               indi_2 = acculate_num_cluster(cluster_2,1) + ceil(rand() * number_in_cluster(cluster_2,1));                tem = rand();               if rand() < 0.5 %use center                  indi_temp(1,:) = tem * centers(cluster_1,:) + (1-tem) * centers(cluster_2,:);                else            % use randomly selected individuals from each cluster                              indi_temp(1,:) = tem * popu_sorted(indi_1,:) + (1-tem) * popu_sorted(indi_2,:);                end          end                    stepSize = logsig(((0.5*max_iteration - n_iteration)/20)) * rand(1,n_d);          indi_temp(1,:) = indi_temp(1,:) + stepSize .* normrnd(0,1,1,n_d);          % if better than the previous one, replace it          fv = fun(indi_temp);          if fv < fitness_popu(idx,1)  % better than the previous one, replace             fitness_popu(idx,1) = fv;             popu(idx,:) = indi_temp(1,:);          end      end      % keep the best for each cluster      for idx = 1:n_c          popu(best(idx,1),:) = centers_copy(idx,:);            fitness_popu(best(idx,1),1) = fit_values(idx,1);      end      n_iteration = n_iteration +1;      % record the best fitness in each iteration      best_fitness(n_iteration, 1) = min(fit_values);end