蜣螂优化算法（DBO）优化支持向量机（SVM）

% ----------------------------------------------------------------------------------------------------------- % Dung Beetle Optimizer: (DBO) (demo) % Programmed by Jian-kai Xue % Updated 28 Nov. 2022. % % This is a simple demo version only implemented the basic % idea of the DBO for solving the unconstrained problem. % The details about DBO are illustratred in the following paper. % (To cite this article): % Jiankai Xue & Bo Shen (2022) Dung beetle optimizer: a new meta-heuristic % algorithm for global optimization. The Journal of Supercomputing, DOI: % 10.1007/s11227-022-04959-6 %%%%%%%%%%% %% QQ此│439115722 %%%%%%%%%%% function [fMin , bestX, Convergence_curve ] = DBO(pop, M,c,d,dim,data ) P_percent = 0.2; % The population size of producers accounts for "P_percent" percent of the total population size %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% pNum = round( pop * P_percent ); % The population size of the producers lb= c.*ones( 1,dim ); % Lower limit/bounds/ a vector ub= d.*ones( 1,dim ); % Upper limit/bounds/ a vector %Initialization for i = 1 : pop x( i, : ) = lb + (ub - lb) .* rand( 1, dim ); fit( i ) = fobj( x( i, : ),data) ; end pFit = fit; pX = x; XX=pX; [ fMin, bestI ] = min( fit ); % fMin denotes the global optimum fitness value bestX = x( bestI, : ); % bestX denotes the global optimum position corresponding to fMin % Start updating the solutions. for t = 1 : M [fmax,B]=max(fit); worse= x(B,:); r2=rand(1); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% for i = 1 : pNum if(r2<0.9) r1=rand(1); a=rand(1,1); if (a>0.1) a=1; else a=-1; end x( i , : ) = pX( i , :)+0.3*abs(pX(i , : )-worse)+a*0.1*(XX( i , :)); % Equation (1) else aaa= randperm(180,1); if ( aaa==0 ||aaa==90 ||aaa==180 ) x( i , : ) = pX( i , :); end theta= aaa*pi/180; x( i , : ) = pX( i , :)+tan(theta).*abs(pX(i , : )-XX( i , :)); % Equation (2) end x( i , : ) = Bounds( x(i , : ), lb, ub ); fit( i ) = fobj( x( i, : ),data) ; end [ fMMin, bestII ] = min( fit ); % fMin denotes the current optimum fitness value bestXX = x( bestII, : ); % bestXX denotes the current optimum position R=1-t/M; % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Xnew1 = bestXX.*(1-R); Xnew2 =bestXX.*(1+R); %%% Equation (3) Xnew1= Bounds( Xnew1, lb, ub ); Xnew2 = Bounds( Xnew2, lb, ub ); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Xnew11 = bestX.*(1-R); Xnew22 =bestX.*(1+R); %%% Equation (5) Xnew11= Bounds( Xnew11, lb, ub ); Xnew22 = Bounds( Xnew22, lb, ub ); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% for i = ( pNum + 1 ) :12 % Equation (4) x( i, : )=bestXX+((rand(1,dim)).*(pX( i , : )-Xnew1)+(rand(1,dim)).*(pX( i , : )-Xnew2)); x(i, : ) = Bounds( x(i, : ), Xnew1, Xnew2 ); fit( i ) = fobj( x( i, : ),data) ; end for i = 13: 19 % Equation (6) x( i, : )=pX( i , : )+((randn(1)).*(pX( i , : )-Xnew11)+((rand(1,dim)).*(pX( i , : )-Xnew22))); x(i, : ) = Bounds( x(i, : ),lb, ub); fit( i ) = fobj( x( i, : ),data) ; end for j = 20 : pop % Equation (7) x( j,: )=bestX+randn(1,dim).*((abs(( pX(j,: )-bestXX)))+(abs(( pX(j,: )-bestX))))./2; x(j, : ) = Bounds( x(j, : ), lb, ub ); fit( j ) = fobj( x( j, : ),data) ; end % Update the individual's best fitness vlaue and the global best fitness value XX=pX; for i = 1 : pop if ( fit( i ) < pFit( i ) ) pFit( i ) = fit( i ); pX( i, : ) = x( i, : ); end if( pFit( i ) < fMin ) % fMin= pFit( i ); fMin= pFit( i ); bestX = pX( i, : ); % a(i)=fMin; end end Convergence_curve(t)=fMin; end % Application of simple limits/bounds function s = Bounds( s, Lb, Ub) % Apply the lower bound vector temp = s; I = temp < Lb; temp(I) = Lb(I); % Apply the upper bound vector J = temp > Ub; temp(J) = Ub(J); % Update this new move s = temp; function S = Boundss( SS, LLb, UUb) % Apply the lower bound vector temp = SS; I = temp < LLb; temp(I) = LLb(I); % Apply the upper bound vector J = temp > UUb; temp(J) = UUb(J); % Update this new move S = temp; %---------------------------------------------------------------------------------------------------------------------------