✅作者简介：热爱科研的Matlab仿真开发者，修心和技术同步精进，matlab项目合作可私信。
🍎个人主页：Matlab科研工作室
🍊个人信条：格物致知。
更多Matlab仿真内容点击👇
⛄ 内容介绍
聚类分析是近年来迅速发展起来的一种新兴的数据处理技术,它在许多领域有着广泛的应用,尤其是在数据挖掘领域.本文实现基于蝙蝠算法实现数据聚类。
⛄ 部分代码
%%-------------------------------------------------------------------------
% (Citation details): %
% J. Senthilnath, Sushant Kulkarni, J.A. Benediktsson and X.S. Yang %
% (2016) "", %
% IEEE Letters for Geoscience and Remote Sensing Letters, %
% Vol. 13, No. 4, pp.599?03. %
%%-------------------------------------------------------------------------
function [best]=Bat_Algorithm(traindat,limits,v)
% Default parameters
n= 5; % Population size, typically 10 to 40
N_gen= 50; % Number of generations
% Iteration parameters
A=zeros(n,1); % Loudness (constant or decreasing)
r= zeros(n,1); % Pulse rate (constant or decreasing)
% The frequency range determines the scalings
% These values need to be changed as necessary
Qmin=0; % Frequency minimum
Qmax=2; % Frequency maximum
% Dimension of the search variables
d=v; % Number of dimensions
N_iter=0; % Total number of function evaluations
% Upper limit/bounds/ a vector
Ub=limits(1,:);
% Lower limit/bounds/ a vector
Lb=limits(2,:);
% Initializing arrays
Q=zeros(n,1); % Frequency of Bats
v=zeros(n,d); % Velocities of Bats
% Initialize the population/solutions
for i=1:n,
Sol(i,:)=Lb+(Ub-Lb).*rand(1,d);
Fitness(i)=Fun(Sol(i,:));
r(i)=rand(1);
A(i)=1+rand(1);
end
r0=r;
plot(Sol(:,1),Sol(:,2),'gs', 'LineWidth',1.5); % plot initial solutions for visualization
hold on;
% Find the initial best solution
% Here, probable center with least distance in cluster
[fmin, I]=min(Fitness);
best=Sol(I,:);
% Start of iterations -- Bat Algorithm (essential part) %
for t=1:N_gen
% Loop over all bats/solutions
for i=1:n
Q(i)=Qmin+(Qmax-Qmin)*rand;
v(i,:)=v(i,:)+(Sol(i,:)-best)*Q(i);
S(i,:)=Sol(i,:)+v(i,:);
tem(1,:) = Sol(i,:); % Solution before movement
% Apply simple bounds/limits
S(i,:)=simplebounds(S(i,:),Lb,Ub);
% Pulse rate
if rand>r(i)
% The factor 0.001 limits the step sizes of random walks
S(i,:)=S(i,:)+0.001*randn(1,d);
S(i,:)=simplebounds(S(i,:),Lb,Ub);
end
% Evaluate new solutions
Fnew=Fun(S(i,:));
% Update if the solution improves, or not too loud
if (Fnew<=Fitness(i)) && (rand<A(i))
Sol(i,:)=S(i,:); % Replace initial solution with improvised solution
tem(2,:) = S(i,:); % Solution after movement
Fitness(i)=Fnew; % Replace initial fitness with improvised fitness
A(i)=0.9*A(i); % Update the Loudness of Bats
r(i)=r0(i)*(1-exp(-0.9*N_gen)); % Update the Pitch of Bats
end
% Find and update the current best solution
if Fnew<=fmin,
best=S(i,:);
fmin=Fnew;
end
% plot the movement of the solutions
pause(0.005)
hold on;
plot(tem(:,1),tem(:,2),'k:');
end
N_iter=N_iter+n;
end
% plot the final optimal cluster center
plot(best(1),best(2),'k*', 'LineWidth',3)
legend('Class 1 Training','Class 2 Training','Class 1 Testing','Class 2 Testing ','Agents','Agents Movement','Location','NorthEastOutside')
text(33,23,'* Cluster Centers', 'FontName','Times','Fontsize',12)
% Output/display
disp(['Number of evaluations: ',num2str(N_iter)]);
disp(['Best =',num2str(best),' fmin=',num2str(fmin)]);
% Application of simple limits/bounds
function s=simplebounds(s,Lb,Ub)
% Apply the lower bound vector
ns_tmp=s;
tt=ns_tmp<Lb;
ns_tmp(tt)=Lb(tt);
% Apply the upper bound vector
J=ns_tmp>Ub;
ns_tmp(J)=Ub(J);
% Update this new move
s=ns_tmp;
end
⛄ 运行结果
⛄ 参考文献
[1]邹全, 常程威, 贾月月. 基于MATLAB的就业数据的聚类分析[J]. 考试周刊, 2016(53):2.
