【数据分类问题】基于麻雀搜索算法优化支持向量机的数据分类方法SSA-SVM分类算法【matlab代码】

function [fMin, bestX, Convergence_curve] = SSA(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 ) = fun( x( i, : ),data ) ; end pFit = fit; pX = x; % The individual's best position corresponding to the pFit [ 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 [~, sortIndex] = sort( pFit );% Sort [fmax,B] = max( pFit ); worse = x(B,:); r2 = rand(1); %% 发现者位置更新 if(r2<0.8) for i = 1 : pNum % Equation (3) r1=rand(1); x( sortIndex( i ), : ) = pX( sortIndex( i ), : )*exp(-(i)/(r1*M)); x( sortIndex( i ), : ) = Bounds( x( sortIndex( i ), : ), lb, ub ); fit( sortIndex( i ) ) = fun( x( sortIndex( i ), : ),data ); end else for i = 1 : pNum x( sortIndex( i ), : ) = pX( sortIndex( i ), : )+randn(1)*ones(1,dim); x( sortIndex( i ), : ) = Bounds( x( sortIndex( i ), : ), lb, ub ); fit( sortIndex( i ) ) = fun( x( sortIndex( i ), : ),data ); end end [~, bestII] = min(fit); bestXX = x(bestII,:); %% 跟随者位置更新 for i = ( pNum + 1 ) : pop % Equation (4) A=floor(rand(1,dim)*2)*2-1; if( i>(pop/2)) x( sortIndex(i ), : )=randn(1)*exp((worse-pX( sortIndex( i ), : ))/(i)^2); else x( sortIndex( i ), : )=bestXX+(abs(( pX( sortIndex( i ), : )-bestXX)))*(A'*(A*A')^(-1))*ones(1,dim); end x( sortIndex( i ), : ) = Bounds( x( sortIndex( i ), : ), lb, ub ); fit( sortIndex( i ) ) = fun( x( sortIndex( i ), : ),data ); end c=randperm(numel(sortIndex)); b=sortIndex(c(1:20)); %% 警戒值位置更新 for j = 1 : length(b) % Equation (5) if( pFit( sortIndex( b(j) ) )>(fMin) ) x( sortIndex( b(j) ), : )=bestX+(randn(1,dim)).*(abs(( pX( sortIndex( b(j) ), : ) -bestX))); else x( sortIndex( b(j) ), : ) =pX( sortIndex( b(j) ), : )+(2*rand(1)-1)*(abs(pX( sortIndex( b(j) ), : )-worse))/ ( pFit( sortIndex( b(j) ) )-fmax+1e-50); end x( sortIndex(b(j) ), : ) = Bounds( x( sortIndex(b(j) ), : ), lb, ub ); fit( sortIndex( b(j) ) ) = fun( x( sortIndex( b(j) ), : ),data ); end 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 ); bestX = pX( i, : ); 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;