[数据结构与算法]【SVM预测】基于海洋捕食算法优化支持向量机SVM实现数据预测附matlab代码

1 简介

提出一种基于海洋捕食算法(MPA)和支持向量机(SVM)的股价预测方法.针对SVM预测模型参数难以确定的问题,采用MPA算法对SVM中惩罚因子及核函数参数进行优化,构建MPA-SVM股价预测模型。

2 部分代码

%_________________________________________________________________________%  Marine Predators Algorithm source code (Developed in MATLAB R2015a)%?function [Top_predator_fit,Top_predator_pos,Convergence_curve]=MPA(SearchAgents_no,Max_iter,lb,ub,dim,fobj)??Top_predator_pos=zeros(1,dim);Top_predator_fit=inf; ?Convergence_curve=zeros(1,Max_iter);stepsize=zeros(SearchAgents_no,dim);fitness=inf(SearchAgents_no,1);??Prey=initialization(SearchAgents_no,dim,ub,lb);  Xmin=repmat(ones(1,dim).*lb,SearchAgents_no,1);Xmax=repmat(ones(1,dim).*ub,SearchAgents_no,1);         ?Iter=0;FADs=0.2;P=0.5;?while Iter<Max_iter         %------------------- Detecting top predator -----------------     for i=1:size(Prey,1)              Flag4ub=Prey(i,:)>ub;    Flag4lb=Prey(i,:)<lb;        Prey(i,:)=(Prey(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;                                fitness(i,1)=fobj(Prey(i,:));                          if fitness(i,1)<Top_predator_fit        Top_predator_fit=fitness(i,1);        Top_predator_pos=Prey(i,:);     end           end          %------------------- Marine Memory saving -------------------      if Iter==0   fit_old=fitness;    Prey_old=Prey; end       Inx=(fit_old<fitness);  Indx=repmat(Inx,1,dim);  Prey=Indx.*Prey_old+~Indx.*Prey;  fitness=Inx.*fit_old+~Inx.*fitness;          fit_old=fitness;    Prey_old=Prey;?     %------------------------------------------------------------         Elite=repmat(Top_predator_pos,SearchAgents_no,1);  %(Eq. 10)  CF=(1-Iter/Max_iter)^(2*Iter/Max_iter);                              RL=0.05*levy(SearchAgents_no,dim,1.5);   %Levy random number vector RB=randn(SearchAgents_no,dim);          %Brownian random number vector             for i=1:size(Prey,1)     for j=1:size(Prey,2)               R=rand();          %------------------ Phase 1 (Eq.12) -------------------        if Iter<Max_iter/3           stepsize(i,j)=RB(i,j)*(Elite(i,j)-RB(i,j)*Prey(i,j));                              Prey(i,j)=Prey(i,j)+P*R*stepsize(i,j);                        %--------------- Phase 2 (Eqs. 13 & 14)----------------       elseif Iter>Max_iter/3 && Iter<2*Max_iter/3                    if i>size(Prey,1)/2            stepsize(i,j)=RB(i,j)*(RB(i,j)*Elite(i,j)-Prey(i,j));            Prey(i,j)=Elite(i,j)+P*CF*stepsize(i,j);          else            stepsize(i,j)=RL(i,j)*(Elite(i,j)-RL(i,j)*Prey(i,j));                                 Prey(i,j)=Prey(i,j)+P*R*stepsize(i,j);           end                    %----------------- Phase 3 (Eq. 15)-------------------       else                       stepsize(i,j)=RL(i,j)*(RL(i,j)*Elite(i,j)-Prey(i,j));            Prey(i,j)=Elite(i,j)+P*CF*stepsize(i,j);             end        end                                           end                 %------------------ Detecting top predator ------------------          for i=1:size(Prey,1)              Flag4ub=Prey(i,:)>ub;      Flag4lb=Prey(i,:)<lb;      Prey(i,:)=(Prey(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;      fitness(i,1)=fobj(Prey(i,:));              if fitness(i,1)<Top_predator_fit          Top_predator_fit=fitness(i,1);         Top_predator_pos=Prey(i,:);      end       end             %---------------------- Marine Memory saving ----------------     if Iter==0    fit_old=fitness;    Prey_old=Prey; end         Inx=(fit_old<fitness);    Indx=repmat(Inx,1,dim);    Prey=Indx.*Prey_old+~Indx.*Prey;    fitness=Inx.*fit_old+~Inx.*fitness;            fit_old=fitness;    Prey_old=Prey;?     %---------- Eddy formation and FADs? effect (Eq 16) -----------                                if rand()<FADs     U=rand(SearchAgents_no,dim)<FADs;                                                                                                   Prey=Prey+CF*((Xmin+rand(SearchAgents_no,dim).*(Xmax-Xmin)).*U);?  else     r=rand();  Rs=size(Prey,1);     stepsize=(FADs*(1-r)+r)*(Prey(randperm(Rs),:)-Prey(randperm(Rs),:));     Prey=Prey+stepsize;  end                                                          Iter=Iter+1;    Convergence_curve(Iter)=Top_predator_fit;        end??

3 仿真结果

4 参考文献

[1]束诗雨. 基于集成学习的支持向量机预测优化算法及其应用[D]. 东华大学.?

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