基于Matlab怎么实现鲸鱼优化算法
生活随笔
收集整理的這篇文章主要介紹了
基于Matlab怎么实现鲸鱼优化算法
小編覺得挺不錯的,現在分享給大家,幫大家做個參考.
這篇文章主要介紹“基于Matlab怎么實現鯨魚優化算法”，在日常操作中，相信很多人在基于Matlab怎么實現鯨魚優化算法問題上存在疑惑，小編查閱了各式資料，整理出簡單好用的操作方法，希望對大家解答”基于Matlab怎么實現鯨魚優化算法”的疑惑有所幫助！接下來，請跟著小編一起來學習吧！
1.鯨魚優化算法建模
鯨魚優化算法(WOA)是澳大利亞學者Mirjaili等于2016年提出的群體智能優化算法，根據座頭鯨的捕獵行為實現優化搜索的目的。其中，每個鯨魚可以看作一個粒子，每個粒子作為不同的決策變量。WOA的實現過程主要包括包圍獵物、螺旋狩獵和隨機搜索3個階段，其數學模型如下：
1.1 包圍獵物
1.2螺旋狩獵
1.3搜索獵物
1.4 算法流程圖
2.Matlab代碼實現
2.1 結果
2.2 代碼
clearall
clc
SearchAgents_no=30;
Function_name='F1';%NameofthetestfunctionthatcanbefromF1toF23(Table1,2,3inthepaper)
%Max_iteration=500;%Maximumnumbefofiterations
Max_iteration=500;
%Loaddetailsoftheselectedbenchmarkfunction
[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',[269240660290])
%Drawsearchspace
subplot(1,2,1);
func_plot(Function_name);
title('Parameterspace')
xlabel('x_1');
ylabel('x_2');
zlabel([Function_name,'(x_1,x_2)'])
%Drawobjectivespace
subplot(1,2,2);
semilogy(WOABAT_cg_curve,'Color','r')
title('Objectivespace')
xlabel('Iteration');
ylabel('Bestscoreobtainedsofar');
axistight
gridon
boxon
legend('WOABAT')
%display(['ThebestsolutionobtainedbyWOABATis:',num2str(Best_pos)]);
display(['ThebestoptimalvalueoftheobjectivefuncitonfoundbyWOAis:',num2str(Best_score)]);
%display(num2str(Best_score));
%TheWhaleOptimizationAlgorithm function[Leader_score,Leader_pos,Convergence_curve]=WOABAT(SearchAgents_no,Max_iter,lb,ub,dim,fobj) %initializepositionvectorandscorefortheleader Leader_pos=zeros(1,dim); Leader_score=inf;%changethisto-infformaximizationproblems %Initializethepositionsofsearchagents Positions=initialization(SearchAgents_no,dim,ub,lb); Convergence_curve=zeros(1,Max_iter); %batalgorithmaddition Qmin=0;%Frequencyminimum Qmax=2;%Frequencymaximum Q=zeros(SearchAgents_no,1);%Frequency v=zeros(SearchAgents_no,dim);%Velocities r=0.5; A1=0.5; t=0;%Loopcounter %summ=0; %Mainloop whilet<Max_iter fori=1:size(Positions,1) %Returnbackthesearchagentsthatgobeyondtheboundariesofthesearchspace Flag4ub=Positions(i,:)>ub; Flag4lb=Positions(i,:)<lb; Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb; %Calculateobjectivefunctionforeachsearchagent fitness=fobj(Positions(i,:)); %Updatetheleader iffitness<Leader_score%Changethisto>formaximizationproblem Leader_score=fitness;%Updatealpha Leader_pos=Positions(i,:); end end a=2-t*((2)/Max_iter);%adecreaseslinearlyfron2to0inEq.(2.3) %a2linearlydicreasesfrom-1to-2tocalculatetinEq.(3.12) a2=-1+t*((-1)/Max_iter); %UpdatethePositionofsearchagents fori=1:size(Positions,1) r1=rand();%r1isarandomnumberin[0,1] r2=rand();%r2isarandomnumberin[0,1] A=2*a*r1-a; C=2*r2; b=1; l=(a2-1)*rand+1; p=rand(); forj=1:size(Positions,2) ifp<0.5 ifabs(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 ifrand>r %Thefactor0.001limitsthestepsizesofrandomwalks z(i,:)=Leader_pos(j)+0.001*randn(1,dim); end %Evaluatenewsolutions Fnew=fobj(z(i,:)); %Updateifthesolutionimproves,ornottooloud if(Fnew<=fitness)&&(rand<A1) Positions(i,:)=z(i,:); fitness=Fnew; end elseifabs(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 ifrand>r %Thefactor0.001limitsthestepsizesofrandomwalks z(i,:)=Leader_pos(j)+0.001*randn(1,dim); end %Evaluatenewsolutions Fnew=fobj(z(i,:)); %Updateifthesolutionimproves,ornottooloud if(Fnew<=fitness)&&(rand<A1) Positions(i,:)=z(i,:); fitness=Fnew; end end elseifp>=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; [tLeader_score] end
%Thisfunctiondrawthebenchmarkfunctions functionfunc_plot(func_name) [lb,ub,dim,fobj]=Get_Functions_details(func_name); switchfunc_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=[]; fori=1:L forj=1:L ifstrcmp(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 ifstrcmp(func_name,'F15')==1 f(i,j)=fobj([x(i),y(j),0,0]); end ifstrcmp(func_name,'F19')==1 f(i,j)=fobj([x(i),y(j),0]); end ifstrcmp(func_name,'F20')==1 f(i,j)=fobj([x(i),y(j),0,0,0,0]); end ifstrcmp(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) switchF 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 functiono=F1(x) o=sum(x.^2); end %F2 functiono=F2(x) o=sum(abs(x))+prod(abs(x)); end %F3 functiono=F3(x) dim=size(x,2); o=0; fori=1:dim o=o+sum(x(1:i))^2; end end %F4 functiono=F4(x) o=max(abs(x)); end %F5 functiono=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 functiono=F6(x) o=sum(abs((x+.5)).^2); end %F7 functiono=F7(x) dim=size(x,2); o=sum([1:dim].*(x.^4))+rand; end %F8 functiono=F8(x) o=sum(-x.*sin(sqrt(abs(x)))); end %F9 functiono=F9(x) dim=size(x,2); o=sum(x.^2-10*cos(2*pi.*x))+10*dim; end %F10 functiono=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 functiono=F11(x) dim=size(x,2); o=sum(x.^2)/4000-prod(cos(x./sqrt([1:dim])))+1; end %F12 functiono=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 functiono=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 functiono=F14(x) aS=[-32-1601632-32-1601632-32-1601632-32-1601632-32-1601632;,... -32-32-32-32-32-16-16-16-16-160000016161616163232323232]; forj=1:25 bS(j)=sum((x'-aS(:,j)).^6); end o=(1/500+sum(1./([1:25]+bS))).^(-1); end %F15 functiono=F15(x) aK=[.1957.1947.1735.16.0844.0627.0456.0342.0323.0235.0246]; bK=[.25.51246810121416];bK=1./bK; o=sum((aK-((x(1).*(bK.^2+x(2).*bK))./(bK.^2+x(3).*bK+x(4)))).^2); end %F16 functiono=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 functiono=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 functiono=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 functiono=F19(x) aH=[31030;.11035;31030;.11035];cH=[11.233.2]; pH=[.3689.117.2673;.4699.4387.747;.1091.8732.5547;.03815.5743.8828]; o=0; fori=1:4 o=o-cH(i)*exp(-(sum(aH(i,:).*((x-pH(i,:)).^2)))); end end %F20 functiono=F20(x) aH=[103173.51.78;.051017.1814;33.51.710178;178.0510.114]; cH=[11.233.2]; 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; fori=1:4 o=o-cH(i)*exp(-(sum(aH(i,:).*((x-pH(i,:)).^2)))); end end %F21 functiono=F21(x) aSH=[4444;1111;8888;6666;3737;2929;5533;8181;6262;73.673.6]; cSH=[.1.2.2.4.4.6.3.7.5.5]; o=0; fori=1:5 o=o-((x-aSH(i,:))*(x-aSH(i,:))'+cSH(i))^(-1); end end %F22 functiono=F22(x) aSH=[4444;1111;8888;6666;3737;2929;5533;8181;6262;73.673.6]; cSH=[.1.2.2.4.4.6.3.7.5.5]; o=0; fori=1:7 o=o-((x-aSH(i,:))*(x-aSH(i,:))'+cSH(i))^(-1); end end %F23 functiono=F23(x) aSH=[4444;1111;8888;6666;3737;2929;5533;8181;6262;73.673.6]; cSH=[.1.2.2.4.4.6.3.7.5.5]; o=0; fori=1:10 o=o-((x-aSH(i,:))*(x-aSH(i,:))'+cSH(i))^(-1); end end functiono=Ufun(x,a,k,m) o=k.*((x-a).^m).*(x>a)+k.*((-x-a).^m).*(x<(-a)); end
%Thisfunctioninitializethefirstpopulationofsearchagents functionPositions=initialization(SearchAgents_no,dim,ub,lb) Boundary_no=size(ub,2);%numnberofboundaries %Iftheboundariesofallvariablesareequalanduserenterasingle %numberforbothubandlb ifBoundary_no==1 Positions=rand(SearchAgents_no,dim).*(ub-lb)+lb; end %Ifeachvariablehasadifferentlbandub ifBoundary_no>1 fori=1:dim ub_i=ub(i); lb_i=lb(i); Positions(:,i)=rand(SearchAgents_no,1).*(ub_i-lb_i)+lb_i; end end
總結
以上是生活随笔為你收集整理的基于Matlab怎么实现鲸鱼优化算法的全部內容，希望文章能夠幫你解決所遇到的問題。
- 上一篇： 《守望先锋 2》×《一拳超人》
- 下一篇： Wannacry分析