FA.rar_SVM_optimization_优化 svm_优化C和g_材料

%%% End of the part to be modified -------------------%%% %%% --------------------------------------------------%%% %%% Do not modify the following codes unless you want %%% %%% to improve its performance etc %%% % ------------------------------------------------------- % ===Start of the Firefly Algorithm Implementation ====== % Lb = lower bounds/limits % Ub = upper bounds/limits % para == optional (to control the Firefly algorithm) % Outputs: nbest = the best solution found so far % fbest = the best objective value % NumEval = number of evaluations: n*MaxGeneration % Optional: % The alpha can be reduced (as to reduce the randomness) % --------------------------------------------------------- % 算法主程序开始 Start FA function [nbest,fbest]=ffa_mincon_svm(costfhandle,u0, Lb, Ub, para,train_wine_labels,train_wine,test_wine_labels,test_wine) % 检查输入参数 Check input parameters (otherwise set as default values) if nargin<5 para=[20 100 0.25 0.20 1]; end if nargin<4 Ub=[]; end if nargin<3 Lb=[]; end if nargin<2 disp('Usuage: FA_mincon(@cost,u0,Lb,Ub,para)'); end % n=number of fireflies % MaxGeneration=number of pseudo time steps % ------------------------------------------------ % alpha=0.25; % Randomness 0--1 (highly random) % betamn=0.20; % minimum value of beta % gamma=1; % Absorption coefficient % ------------------------------------------------ n=para(1); MaxGeneration=para(2); alpha=para(3); betamin=para(4); gamma=para(5); % 检查上界向量与下界向量长度是否相同 Check if the upper bound & lower bound are the same size if length(Lb) ~=length(Ub) disp('Simple bounds/limits are improper!') return end % 计算待优化参数维度 Calcualte dimension d=length(u0); % 初始化目标函数值 Initial values of an array zn=ones(n,1)*10^100; % ------------------------------------------------ % 初始化萤火虫位置 generating the initial locations of n fireflies [ns,Lightn]=init_ffa(n,d,Lb,Ub,u0); for k=1:MaxGeneration % 迭代开始 % 更新alpha（可选）This line of reducing alpha is optional alpha=alpha_new(alpha,MaxGeneration); % 对每个萤火虫计算目标函数值 Evaluate new solutions (for all n fireflies) for i=1:n zn(i)=costfhandle(ns(i,:),train_wine_labels,train_wine,test_wine_labels,test_wine); Lightn(i)=zn(i); end % 根据亮度排序 Ranking fireflies by their light intensity/objectives [Lightn,Index]=sort(zn); ns_tmp=ns; for i=1:n ns(i,:)=ns_tmp(Index(i),:); end %% 找出当前最优 Find the current best nso=ns; Lighto=Lightn; nbest=ns(1,:); Lightbest=Lightn(1); % 另存最优值 For output only fbest=Lightbest; % 向较优方向移动 Move all fireflies to the better locations [ns]=ffa_move(n,d,ns,Lightn,nso,Lighto,alpha,betamin,gamma,Lb,Ub); end % ----- All the subfunctions are listed here ------------ % 初始化萤火虫位置 The initial locations of n fireflies function [ns,Lightn]=init_ffa(n,d,Lb,Ub,u0) ns=zeros(n,d); if ~isempty(Lb) % 如果参数界限不为空 if there are bounds/limits for i=1:n ns(i,:)=Lb+(Ub-Lb).*rand(1,d); % 则在取值范围内随机取值 end else % 如果没有设置参数界限 for i=1:n ns(i,:)=u0+randn(1,d); % 在原有参数上加白噪声 end end % 初始化目标函数 initial value before function evaluations Lightn=ones(n,1)*10^100; % Move all fireflies toward brighter ones function [ns]=ffa_move(n,d,ns,Lightn,nso,Lighto,alpha,betamin,gamma,Lb,Ub) % 参数取值范围绝对值 Scaling of the system scale=abs(Ub-Lb); % 更新萤火虫 Updating fireflies for i=1:n % The attractiveness parameter beta=exp(-gamma*r) for j=1:n r=sqrt(sum((ns(i,:)-ns(j,:)).^2)); % Update moves if Lightn(i)>Lighto(j) % 如果i比j亮度更强 Brighter and more attractive beta0=1; beta=(beta0-betamin)*exp(-gamma*r.^2)+betamin; tmpf=alpha.*(rand(1,d)-0.5).*scale; ns(i,:)=ns(i,:).*(1-beta)+nso(j,:).*beta+tmpf; end end % end for j end % end for i % 防止越界 Check if the updated solutions/locations are within limits [ns]=findlimits(n,ns,Lb,Ub); % This function is optional, as it is not in the original FA % The idea to reduce randomness is to increase the convergence, % however, if you reduce randomness too quickly, then premature % convergence can occur. So use with care. % alpha参数更新函数 function alpha=alpha_new(alpha,NGen) % alpha_n=alpha_0(1-delta)^NGen=10^(-4); % alpha_0=0.9 delta=1-(10^(-4)/0.9)^(1/NGen); alpha=(1-delta)*alpha; % 防止越界 Make sure the fireflies are within the bounds/limits function [ns]=findlimits(n,ns,Lb,Ub) for i=1:n % Apply the lower bound ns_tmp=ns(i,:); I=ns_tmp<Lb; ns_tmp(I)=Lb(I); % Apply the upper bounds J=ns_tmp>Ub; ns_tmp(J)=Ub(J); % Update this new move ns(i,:)=ns_tmp; end