【高创新】基于沙猫群优化算法SCSO-Transformer-BiLSTM实现故障识别Matlab实现.rar

%_________________________________________________________________________% % Rat Swarm Optimizer (RSO) % % % % Developed in MATLAB R2019b % % % % Designed and Developed: Dr. Gaurav Dhiman % % % % E-Mail: gdhiman0001@gmail.com % % gaurav.dhiman@ieee.org % % % % Homepage: http://www.dhimangaurav.com % % % % Published paper: G. Dhiman et al. % % A novel algorithm for global optimization: Rat Swarm Optimizer % % Jounral of Ambient Intelligence and Humanized Computing % % DOI: https://doi.org/10.1007/s12652-020-02580-0 % % % %_________________________________________________________________________% function [lowerbound,upperbound,dimension,fitness] = fun_info(F) switch F case 'F1' fitness = @F1; lowerbound=-100; upperbound=100; dimension=30; case 'F2' fitness = @F2; lowerbound=-10; upperbound=10; dimension=30; case 'F3' fitness = @F3; lowerbound=-100; upperbound=100; dimension=30; case 'F4' fitness = @F4; lowerbound=-100; upperbound=100; dimension=30; case 'F5' fitness = @F5; lowerbound=-30; upperbound=30; dimension=30; case 'F6' fitness = @F6; lowerbound=-100; upperbound=100; dimension=30; case 'F7' fitness = @F7; lowerbound=-1.28; upperbound=1.28; dimension=30; case 'F8' fitness = @F8; lowerbound=-500; upperbound=500; dimension=30; case 'F9' fitness = @F9; lowerbound=-5.12; upperbound=5.12; dimension=30; case 'F10' fitness = @F10; lowerbound=-32; upperbound=32; dimension=30; case 'F11' fitness = @F11; lowerbound=-600; upperbound=600; dimension=30; case 'F12' fitness = @F12; lowerbound=-50; upperbound=50; dimension=30; case 'F13' fitness = @F13; lowerbound=-50; upperbound=50; dimension=30; case 'F14' fitness = @F14; lowerbound=-65.536; upperbound=65.536; dimension=2; case 'F15' fitness = @F15; lowerbound=-5; upperbound=5; dimension=4; case 'F16' fitness = @F16; lowerbound=-5; upperbound=5; dimension=2; case 'F17' fitness = @F17; lowerbound=[-5,0]; upperbound=[10,15]; dimension=2; case 'F18' fitness = @F18; lowerbound=-2; upperbound=2; dimension=2; case 'F19' fitness = @F19; lowerbound=0; upperbound=1; dimension=3; case 'F20' fitness = @F20; lowerbound=0; upperbound=1; dimension=6; case 'F21' fitness = @F21; lowerbound=0; upperbound=10; dimension=4; case 'F22' fitness = @F22; lowerbound=0; upperbound=10; dimension=4; case 'F23' fitness = @F23; lowerbound=0; upperbound=10; dimension=4; end end % F1 function R = F1(x) R=sum(x.^2); end % F2 function R = F2(x) R=sum(abs(x))+prod(abs(x)); end % F3 function R = F3(x) dimension=size(x,2); R=0; for i=1:dimension R=R+sum(x(1:i))^2; end end % F4 function R = F4(x) R=max(abs(x)); end % F5 function R = F5(x) dimension=size(x,2); R=sum(100*(x(2:dimension)-(x(1:dimension-1).^2)).^2+(x(1:dimension-1)-1).^2); end % F6 function R = F6(x) R=sum(abs((x+.5)).^2); end % F7 function R = F7(x) dimension=size(x,2); R=sum([1:dimension].*(x.^4))+rand; end % F8 function R = F8(x) R=sum(-x.*sin(sqrt(abs(x)))); end % F9 function R = F9(x) dimension=size(x,2); R=sum(x.^2-10*cos(2*pi.*x))+10*dimension; end % F10 function R = F10(x) dimension=size(x,2); R=-20*exp(-.2*sqrt(sum(x.^2)/dimension))-exp(sum(cos(2*pi.*x))/dimension)+20+exp(1); end % F11 function R = F11(x) dimension=size(x,2); R=sum(x.^2)/4000-prod(cos(x./sqrt([1:dimension])))+1; end % F12 function R = F12(x) dimension=size(x,2); R=(pi/dimension)*(10*((sin(pi*(1+(x(1)+1)/4)))^2)+sum((((x(1:dimension-1)+1)./4).^2).*... (1+10.*((sin(pi.*(1+(x(2:dimension)+1)./4)))).^2))+((x(dimension)+1)/4)^2)+sum(Ufun(x,10,100,4)); end % F13 function R = F13(x) dimension=size(x,2); R=.1*((sin(3*pi*x(1)))^2+sum((x(1:dimension-1)-1).^2.*(1+(sin(3.*pi.*x(2:dimension))).^2))+... ((x(dimension)-1)^2)*(1+(sin(2*pi*x(dimension)))^2))+sum(Ufun(x,5,100,4)); end % F14 function R = 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 R=(1/500+sum(1./([1:25]+bS))).^(-1); end % F15 function R = 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; R=sum((aK-((x(1).*(bK.^2+x(2).*bK))./(bK.^2+x(3).*bK+x(4)))).^2); end % F16 function R = F16(x) R=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 R = F17(x) R=(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 R = F18(x) R=(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 R = 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]; R=0; for i=1:4 R=R-cH(i)*exp(-(sum(aH(i,:).*((x-pH(i,:)).^2)))); end end % F20 function R = 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]; R=0; for i=1:4 R=R-cH(i)*exp(-(sum(aH(i,:).*((x-pH(i,:)).^2)))); end end % F21 function R = 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]; R=0; for i=1:5 R=R-((x-aSH(i,:))*(x-aSH(i,:))'+cSH(i))^(-1); end end % F22 function R = 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]; R=0; for i=1:7 R=R-((x-aSH(i,:))*(x-aSH(i,:))'+cSH(i))^(-1); end end % F23 function R = 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]; R=0;