Regression prediction | MATLAB implements BES-LSSVM vulture algorithm to optimize least squares support vector machine with multiple input and single output

Regression prediction | MATLAB implements BES-LSSVM vulture algorithm to optimize least squares support vector machine with multiple input and single output

predictive effect

basic introduction

MATLAB implements the BES-LSSVM vulture algorithm to optimize the least squares support vector machine with multiple inputs and single outputs.
MATLAB implements the BES-LSSVM vulture algorithm to optimize the least squares support vector machine with multiple inputs and single outputs
1. For the data set, input 6 features and output a variable.
2.main.m is the main file of the main program, and the rest are function files that do not need to be run.
3. The command window outputs MAE, MSE, R2, RMSEP and RPD, and the data and program content can be obtained in the download area.
4. Condor algorithm optimizes the least squares support vector machine and optimizes the RBF kernel functions gam and sig.
Note that the program and data are placed in a folder, and the operating environment is Matlab2018 and above.

programming

• Complete program and data download method 1 (download directly from the resource): MATLAB implements BES-LSSVM vulture algorithm to optimize least squares support vector machine with multiple inputs and single outputs
• Complete program and data download method 2 (subscribe to the "Smart Learning" column, and at the same time get 4 copies of the program included in the "Smart Learning" column, private message me to get the data after subscription): MATLAB implements BES-LSSVM vulture algorithm to optimize the least squares support vector machine Multiple Input Single Output

function [BestSol Convergence_curve timep]=BES(nPop,MaxIt,low,high,dim,fobj)
%nPop: size of population 
%MaxIt:number of iterations 
%low, high : space of Decision variables
%dim : number of Decision variables
%fobj : funcation 
 % paper citation : Alsattar, H. A., Zaidan, A. A., & Zaidan, B. B. (2020). Novel meta-heuristic bald eagle search optimisation algorithm. Artificial Intelligence Review, 53(3), 2237-2264.?
st=cputime;
% Initialize Best Solution
BestSol.cost = inf;
for i=1:nPop
    pop.pos(i,:) = low+(high-low).*rand(1,dim);
     pop.cost(i)=fobj(pop.pos(i,:));
    if pop.cost(i) < BestSol.cost
        BestSol.pos = pop.pos(i,:);
        BestSol.cost = pop.cost(i);
    end
end
 disp(num2str([0 BestSol.cost]))
for t=1:MaxIt
    %%               1- select_space 
    [pop BestSol s1(t)]=select_space(fobj,pop,nPop,BestSol,low,high,dim);
    %%                2- search in space
    [pop BestSol s2(t)]=search_space(fobj,pop,BestSol,nPop,low,high);
    %%                3- swoop
  [pop BestSol s3(t)]=swoop(fobj,pop,BestSol,nPop,low,high);
        Convergence_curve(t)=BestSol.cost;
        disp(num2str([t BestSol.cost]))
    ed=cputime;
    timep=ed-st;
end
function [pop BestSol s1]=select_space(fobj,pop,npop,BestSol,low,high,dim)
Mean=mean(pop.pos);
% Empty Structure for Individuals
empty_individual.pos = [];
empty_individual.cost = [];
lm= 2;
s1=0;
for i=1:npop
    newsol=empty_individual;
    newsol.pos= BestSol.pos+ lm*rand(1,dim).*(Mean - pop.pos(i,:));
    newsol.pos = max(newsol.pos, low);
    newsol.pos = min(newsol.pos, high);
    newsol.cost=fobj(newsol.pos);
    if newsol.cost<pop.cost(i)
       pop.pos(i,:) = newsol.pos;
       pop.cost(i)= newsol.cost;
       s1=s1+1;
         if pop.cost(i) < BestSol.cost
          BestSol.pos= pop.pos(i,:);
         BestSol.cost=pop.cost(i); 
         end
    end
end
function [pop best s1]=search_space(fobj,pop,best,npop,low,high)
Mean=mean(pop.pos);
a=10;
R=1.5;
% Empty Structure for Individuals
empty_individual.pos = [];
empty_individual.cost = [];
s1=0;
for i=1:npop-1
    A=randperm(npop);
pop.pos=pop.pos(A,:);
pop.cost=pop.cost(A);
        [x y]=polr(a,R,npop);
    newsol=empty_individual;
   Step = pop.pos(i,:) - pop.pos(i+1,:);
   Step1=pop.pos(i,:)-Mean;
   newsol.pos = pop.pos(i,:) +y(i)*Step+x(i)*Step1;
    newsol.pos = max(newsol.pos, low);
    newsol.pos = min(newsol.pos, high);
    newsol.cost=fobj(newsol.pos);
    if newsol.cost<pop.cost(i)
       pop.pos(i,:) = newsol.pos;
       pop.cost(i)= newsol.cost;
              s1=s1+1;
                  if pop.cost(i) < best.cost
                 best.pos= pop.pos(i,:);
                best.cost=pop.cost(i); 
            end
    end
end
function [pop best s1]=swoop(fobj,pop,best,npop,low,high)
Mean=mean(pop.pos);
a=10;
R=1.5;
% Empty Structure for Individuals
empty_individual.pos = [];
empty_individual.cost = [];
s1=0;
for i=1:npop
    A=randperm(npop);
pop.pos=pop.pos(A,:);
pop.cost=pop.cost(A);
        [x y]=swoo_p(a,R,npop);
    newsol=empty_individual;
   Step = pop.pos(i,:) - 2*Mean;
   Step1= pop.pos(i,:)-2*best.pos;
   newsol.pos = rand(1,length(Mean)).*best.pos+x(i)*Step+y(i)*Step1;
    newsol.pos = max(newsol.pos, low);
    newsol.pos = min(newsol.pos, high);
    newsol.cost=fobj(newsol.pos);
    if newsol.cost<pop.cost(i)
       pop.pos(i,:) = newsol.pos;
       pop.cost(i)= newsol.cost;
              s1=s1+1;
                  if pop.cost(i) < best.cost
                 best.pos= pop.pos(i,:);
                best.cost=pop.cost(i); 
            end
    end
end
function [xR yR]=swoo_p(a,R,N)
th = a*pi*exp(rand(N,1));
r  =th; %R*rand(N,1);
xR = r.*sinh(th);
yR = r.*cosh(th);
 xR=xR/max(abs(xR));
 yR=yR/max(abs(yR));
 
 function [xR yR]=polr(a,R,N)
%// Set parameters
th = a*pi*rand(N,1);
r  =th+R*rand(N,1);
xR = r.*sin(th);
yR = r.*cos(th);
 xR=xR/max(abs(xR));
 yR=yR/max(abs(yR));

References

[1] https://blog.csdn.net/article/details/126072792?spm=1001.2014.3001.5502
[2] https://blog.csdn.net/article/details/126044265?spm=1001.2014.3001.5502