c/c++语言开发共享基于Matlab实现鲸鱼优化算法的示例代码

clear all   clc   searchagents_no=30;  function_name='f1'; % name of the test function that can be from f1 to f23 (table 1,2,3 in the paper)  % max_iteration=500; % maximum numbef of iterations  max_iteration=500;  % load details of the selected benchmark function  [lb,ub,dim,fobj]=get_functions_details(function_name);     [best_score,best_pos,woabat_cg_curve]=woabat(searchagents_no,max_iteration,lb,ub,dim,fobj);      figure('position',[269   240   660   290])   %draw search space   subplot(1,2,1);   func_plot(function_name);   title('parameter space')   xlabel('x_1');   ylabel('x_2');   zlabel([function_name,'( x_1 , x_2 )'])      %draw objective space   subplot(1,2,2);   semilogy(woabat_cg_curve,'color','r')   title('objective space')   xlabel('iteration');   ylabel('best score obtained so far');      axis tight   grid on   box on   legend('woabat')  %display(['the best solution obtained by woabat is : ', num2str(best_pos)]);   display(['the best optimal value of the objective funciton found by woa is : ', num2str(best_score)]);      %display( num2str(best_score));

% the whale optimization algorithm  function [leader_score,leader_pos,convergence_curve]=woabat(searchagents_no,max_iter,lb,ub,dim,fobj)     % initialize position vector and score for the leader  leader_pos=zeros(1,dim);  leader_score=inf; %change this to -inf for maximization problems        %initialize the positions of search agents  positions=initialization(searchagents_no,dim,ub,lb);     convergence_curve=zeros(1,max_iter);        %bat algorithm addition  qmin=0;         % frequency minimum  qmax=2;         % frequency maximum  q=zeros(searchagents_no,1);   % frequency  v=zeros(searchagents_no,dim);   % velocities  r=0.5;  a1=0.5;  t=0;% loop counter  % summ=0;  % main loop  while t<max_iter      for i=1:size(positions,1)                    % return back the search agents that go beyond the boundaries of the search space          flag4ub=positions(i,:)>ub;          flag4lb=positions(i,:)<lb;          positions(i,:)=(positions(i,:).*(~(flag4ub+flag4lb)))+ub.*flag4ub+lb.*flag4lb;                    % calculate objective function for each search agent          fitness=fobj(positions(i,:));                    % update the leader          if fitness<leader_score % change this to > for maximization problem              leader_score=fitness; % update alpha              leader_pos=positions(i,:);          end                end            a=2-t*((2)/max_iter); % a decreases linearly fron 2 to 0 in eq. (2.3)            % a2 linearly dicreases from -1 to -2 to calculate t in eq. (3.12)      a2=-1+t*((-1)/max_iter);            % update the position of search agents       for i=1:size(positions,1)          r1=rand(); % r1 is a random number in [0,1]          r2=rand(); % r2 is a random number in [0,1]                    a=2*a*r1-a;            c=2*r2;                                   b=1;                        l=(a2-1)*rand+1;                      p = rand();                           for j=1:size(positions,2)                            if p<0.5                                       if abs(a)>=1                                  rand_leader_index = floor(searchagents_no*rand()+1);                               x_rand = positions(rand_leader_index, :);                               q(i)=qmin+(qmin-qmax)*rand;                               v(i,:)=v(i,j)+(x_rand(j)-leader_pos(j))*q(i);                              z(i,:)= positions(i,:)+ v(i,:);                                                                                          %%%% problem                                   if rand>r                                  % the factor 0.001 limits the step sizes of random walks                                   z (i,:)=leader_pos(j)+0.001*randn(1,dim);                                  end                                       % evaluate new solutions                                      fnew=fobj(z(i,:));                                       % update if the solution improves, or not too loud                                      if (fnew<=fitness) && (rand<a1)                                          positions(i,:)=z(i,:);                                          fitness=fnew;                                      end                     elseif abs(a)<1                               q(i)=qmin+(qmin-qmax)*rand;                               v(i,:)=v(i,j)+(positions(i,:)-leader_pos(j))*q(i);                              z(i,:)= positions(i,:)+ v(i,:);                                                          %%%% problem                                   if rand>r                                  % the factor 0.001 limits the step sizes of random walks                                   z (i,:)=leader_pos(j)+0.001*randn(1,dim);                                  end                                       % evaluate new solutions                                      fnew=fobj(z(i,:));                                       % update if the solution improves, or not too loud                                      if (fnew<=fitness) && (rand<a1)                                          positions(i,:)=z(i,:);                                          fitness=fnew;                                      end                  end                                elseif p>=0.5                                  distance2leader=abs(leader_pos(j)-positions(i,j));                  % eq. (2.5)                  positions(i,j)=distance2leader*exp(b.*l).*cos(l.*2*pi)+leader_pos(j);              end                        end      end      t=t+1;      convergence_curve(t)=leader_score;     [t leader_score]           end

% this function draw the benchmark functions  function func_plot(func_name)     [lb,ub,dim,fobj]=get_functions_details(func_name);     switch func_name       case 'f1'           x=-100:2:100; y=x; %[-100,100]                case 'f2'           x=-100:2:100; y=x; %[-10,10]                case 'f3'           x=-100:2:100; y=x; %[-100,100]                case 'f4'           x=-100:2:100; y=x; %[-100,100]      case 'f5'           x=-200:2:200; y=x; %[-5,5]      case 'f6'           x=-100:2:100; y=x; %[-100,100]      case 'f7'           x=-1:0.03:1;  y=x  %[-1,1]      case 'f8'           x=-500:10:500;y=x; %[-500,500]      case 'f9'           x=-5:0.1:5;   y=x; %[-5,5]          case 'f10'           x=-20:0.5:20; y=x;%[-500,500]      case 'f11'           x=-500:10:500; y=x;%[-0.5,0.5]      case 'f12'           x=-10:0.1:10; y=x;%[-pi,pi]      case 'f13'           x=-5:0.08:5; y=x;%[-3,1]      case 'f14'           x=-100:2:100; y=x;%[-100,100]      case 'f15'           x=-5:0.1:5; y=x;%[-5,5]      case 'f16'           x=-1:0.01:1; y=x;%[-5,5]      case 'f17'           x=-5:0.1:5; y=x;%[-5,5]      case 'f18'           x=-5:0.06:5; y=x;%[-5,5]      case 'f19'           x=-5:0.1:5; y=x;%[-5,5]      case 'f20'           x=-5:0.1:5; y=x;%[-5,5]              case 'f21'           x=-5:0.1:5; y=x;%[-5,5]      case 'f22'           x=-5:0.1:5; y=x;%[-5,5]           case 'f23'           x=-5:0.1:5; y=x;%[-5,5]    end                  l=length(x);  f=[];     for i=1:l      for j=1:l          if strcmp(func_name,'f15')==0 && strcmp(func_name,'f19')==0 && strcmp(func_name,'f20')==0 && strcmp(func_name,'f21')==0 && strcmp(func_name,'f22')==0 && strcmp(func_name,'f23')==0              f(i,j)=fobj([x(i),y(j)]);          end          if strcmp(func_name,'f15')==1              f(i,j)=fobj([x(i),y(j),0,0]);          end          if strcmp(func_name,'f19')==1              f(i,j)=fobj([x(i),y(j),0]);          end          if strcmp(func_name,'f20')==1              f(i,j)=fobj([x(i),y(j),0,0,0,0]);          end                 if strcmp(func_name,'f21')==1 || strcmp(func_name,'f22')==1 ||strcmp(func_name,'f23')==1              f(i,j)=fobj([x(i),y(j),0,0]);          end                end  end     surfc(x,y,f,'linestyle','none');     end

function [lb,ub,dim,fobj] = get_functions_details(f)        switch f      case 'f1'          fobj = @f1;          lb=-100;          ub=100;  %         dim=30;          dim=30;      case 'f2'          fobj = @f2;          lb=-10;          ub=10;          dim=30;                case 'f3'          fobj = @f3;          lb=-100;          ub=100;          dim=30;                case 'f4'          fobj = @f4;          lb=-100;          ub=100;          dim=30;                case 'f5'          fobj = @f5;          lb=-30;          ub=30;          dim=30;                case 'f6'          fobj = @f6;          lb=-100;          ub=100;          dim=30;                case 'f7'          fobj = @f7;          lb=-1.28;          ub=1.28;          dim=30;                case 'f8'          fobj = @f8;          lb=-500;          ub=500;          dim=30;                case 'f9'          fobj = @f9;          lb=-5.12;          ub=5.12;          dim=30;                case 'f10'          fobj = @f10;          lb=-32;          ub=32;          dim=30;                case 'f11'          fobj = @f11;          lb=-600;          ub=600;          dim=30;                case 'f12'          fobj = @f12;          lb=-50;          ub=50;          dim=30;                case 'f13'          fobj = @f13;          lb=-50;          ub=50;          dim=30;                case 'f14'          fobj = @f14;          lb=-65.536;          ub=65.536;          dim=2;                case 'f15'          fobj = @f15;          lb=-5;          ub=5;          dim=4;                case 'f16'          fobj = @f16;          lb=-5;          ub=5;          dim=2;                case 'f17'          fobj = @f17;          lb=[-5,0];          ub=[10,15];          dim=2;                case 'f18'          fobj = @f18;          lb=-2;          ub=2;          dim=2;                case 'f19'          fobj = @f19;          lb=0;          ub=1;          dim=3;                case 'f20'          fobj = @f20;          lb=0;          ub=1;          dim=6;                     case 'f21'          fobj = @f21;          lb=0;          ub=10;          dim=4;                    case 'f22'          fobj = @f22;          lb=0;          ub=10;          dim=4;                    case 'f23'          fobj = @f23;          lb=0;          ub=10;          dim=4;              end     end     % f1     function o = f1(x)  o=sum(x.^2);  end     % f2     function o = f2(x)  o=sum(abs(x))+prod(abs(x));  end     % f3     function o = f3(x)  dim=size(x,2);  o=0;  for i=1:dim      o=o+sum(x(1:i))^2;  end  end     % f4     function o = f4(x)  o=max(abs(x));  end     % f5     function o = f5(x)  dim=size(x,2);  o=sum(100*(x(2:dim)-(x(1:dim-1).^2)).^2+(x(1:dim-1)-1).^2);  end     % f6     function o = f6(x)  o=sum(abs((x+.5)).^2);  end     % f7     function o = f7(x)  dim=size(x,2);  o=sum([1:dim].*(x.^4))+rand;  end     % f8     function o = f8(x)  o=sum(-x.*sin(sqrt(abs(x))));  end     % f9     function o = f9(x)  dim=size(x,2);  o=sum(x.^2-10*cos(2*pi.*x))+10*dim;  end     % f10     function o = f10(x)  dim=size(x,2);  o=-20*exp(-.2*sqrt(sum(x.^2)/dim))-exp(sum(cos(2*pi.*x))/dim)+20+exp(1);  end     % f11     function o = f11(x)  dim=size(x,2);  o=sum(x.^2)/4000-prod(cos(x./sqrt([1:dim])))+1;  end     % f12     function o = f12(x)  dim=size(x,2);  o=(pi/dim)*(10*((sin(pi*(1+(x(1)+1)/4)))^2)+sum((((x(1:dim-1)+1)./4).^2).*...  (1+10.*((sin(pi.*(1+(x(2:dim)+1)./4)))).^2))+((x(dim)+1)/4)^2)+sum(ufun(x,10,100,4));  end     % f13     function o = f13(x)  dim=size(x,2);  o=.1*((sin(3*pi*x(1)))^2+sum((x(1:dim-1)-1).^2.*(1+(sin(3.*pi.*x(2:dim))).^2))+...  ((x(dim)-1)^2)*(1+(sin(2*pi*x(dim)))^2))+sum(ufun(x,5,100,4));  end     % f14     function o = f14(x)  as=[-32 -16 0 16 32 -32 -16 0 16 32 -32 -16 0 16 32 -32 -16 0 16 32 -32 -16 0 16 32;,...  -32 -32 -32 -32 -32 -16 -16 -16 -16 -16 0 0 0 0 0 16 16 16 16 16 32 32 32 32 32];     for j=1:25      bs(j)=sum((x'-as(:,j)).^6);  end  o=(1/500+sum(1./([1:25]+bs))).^(-1);  end  % f15  function o = f15(x)  ak=[.1957 .1947 .1735 .16 .0844 .0627 .0456 .0342 .0323 .0235 .0246];  bk=[.25 .5 1 2 4 6 8 10 12 14 16];bk=1./bk;  o=sum((ak-((x(1).*(bk.^2+x(2).*bk))./(bk.^2+x(3).*bk+x(4)))).^2);  end  % f16  function o = f16(x)  o=4*(x(1)^2)-2.1*(x(1)^4)+(x(1)^6)/3+x(1)*x(2)-4*(x(2)^2)+4*(x(2)^4);  end  % f17  function o = f17(x)  o=(x(2)-(x(1)^2)*5.1/(4*(pi^2))+5/pi*x(1)-6)^2+10*(1-1/(8*pi))*cos(x(1))+10;  end  % f18  function o = f18(x)  o=(1+(x(1)+x(2)+1)^2*(19-14*x(1)+3*(x(1)^2)-14*x(2)+6*x(1)*x(2)+3*x(2)^2))*...      (30+(2*x(1)-3*x(2))^2*(18-32*x(1)+12*(x(1)^2)+48*x(2)-36*x(1)*x(2)+27*(x(2)^2)));  end  % f19  function o = f19(x)  ah=[3 10 30;.1 10 35;3 10 30;.1 10 35];ch=[1 1.2 3 3.2];  ph=[.3689 .117 .2673;.4699 .4387 .747;.1091 .8732 .5547;.03815 .5743 .8828];  o=0;  for i=1:4      o=o-ch(i)*exp(-(sum(ah(i,:).*((x-ph(i,:)).^2))));  end  end  % f20  function o = f20(x)  ah=[10 3 17 3.5 1.7 8;.05 10 17 .1 8 14;3 3.5 1.7 10 17 8;17 8 .05 10 .1 14];  ch=[1 1.2 3 3.2];  ph=[.1312 .1696 .5569 .0124 .8283 .5886;.2329 .4135 .8307 .3736 .1004 .9991;...  .2348 .1415 .3522 .2883 .3047 .6650;.4047 .8828 .8732 .5743 .1091 .0381];  o=0;  for i=1:4      o=o-ch(i)*exp(-(sum(ah(i,:).*((x-ph(i,:)).^2))));  end  end  % f21  function o = f21(x)  ash=[4 4 4 4;1 1 1 1;8 8 8 8;6 6 6 6;3 7 3 7;2 9 2 9;5 5 3 3;8 1 8 1;6 2 6 2;7 3.6 7 3.6];  csh=[.1 .2 .2 .4 .4 .6 .3 .7 .5 .5];  o=0;  for i=1:5      o=o-((x-ash(i,:))*(x-ash(i,:))'+csh(i))^(-1);  end  end     % f22     function o = f22(x)  ash=[4 4 4 4;1 1 1 1;8 8 8 8;6 6 6 6;3 7 3 7;2 9 2 9;5 5 3 3;8 1 8 1;6 2 6 2;7 3.6 7 3.6];  csh=[.1 .2 .2 .4 .4 .6 .3 .7 .5 .5];     o=0;  for i=1:7      o=o-((x-ash(i,:))*(x-ash(i,:))'+csh(i))^(-1);  end  end  % f23  function o = f23(x)  ash=[4 4 4 4;1 1 1 1;8 8 8 8;6 6 6 6;3 7 3 7;2 9 2 9;5 5 3 3;8 1 8 1;6 2 6 2;7 3.6 7 3.6];  csh=[.1 .2 .2 .4 .4 .6 .3 .7 .5 .5];  o=0;  for i=1:10      o=o-((x-ash(i,:))*(x-ash(i,:))'+csh(i))^(-1);  end  end     function o=ufun(x,a,k,m)  o=k.*((x-a).^m).*(x>a)+k.*((-x-a).^m).*(x<(-a));  end

% this function initialize the first population of search agents  function positions=initialization(searchagents_no,dim,ub,lb)     boundary_no= size(ub,2); % numnber of boundaries     % if the boundaries of all variables are equal and user enter a single  % number for both ub and lb  if boundary_no==1      positions=rand(searchagents_no,dim).*(ub-lb)+lb;  end     % if each variable has a different lb and ub  if boundary_no>1      for i=1:dim          ub_i=ub(i);          lb_i=lb(i);          positions(:,i)=rand(searchagents_no,1).*(ub_i-lb_i)+lb_i;      end  end