分类预测 | Matlab实现BES-ELM秃鹰搜索算法优化极限学习机分类预测

分类预测 | Matlab实现BES-ELM秃鹰搜索算法优化极限学习机分类预测

分类效果

基本描述

Matlab实现BES-ELM秃鹰搜索算法优化极限学习机分类预测（完整源码和数据）
1.自带数据为excel数据，多输入，单输出，多分类。
2.直接替换数据即可使用，保证程序可正常运行。
3.程序语言为matlab，程序可出分类效果图，混淆矩阵图
4.注意程序和数据放在一个文件夹。

程序设计

• 完整程序和数据获取方式（资源出直接下载）：Matlab实现BES-ELM秃鹰搜索算法优化极限学习机分类预测。

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
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54

参考资料

[1] https://blog.csdn.net/kjm13182345320/article/details/129036772?spm=1001.2014.3001.5502
[2] https://blog.csdn.net/kjm13182345320/article/details/128690229