我有一个 bat 算法的 Matlab 代码，我编写了这个算法的 java 版本 Bat 算法是一个简单的优化算法，用于找到任何函数的最小值这里是 matlab 代码和我的这个代码的 java 版本
我的这个算法的 java 版本找不到像 matlab 版本那样的最佳结果，我找不到将代码从 matlab 转换为 java 的错误在哪里
谁能帮助我我的错误在哪里？
import java.util.Random;
public class Bat
{
private int n;
private float A, r;
private float Qmin, Qmax;
private int d;
private int NofGen;
private float fmin;
private int fminIndex;
private float Fnew;
private int loopCounter;
private float Q[], V[][], Sol[][], UL_bound[][], fitness[], S[][], Best[];
private Random myRand;
public Bat(
int NBats,
float loudness,
float pulseRate,
float minFreq,
float maxFreq,
int NofGeneration,
int dimension
)
{
n = NBats;
A = loudness;
r = pulseRate;
Qmin = minFreq;
Qmax = maxFreq;
NofGen = NofGeneration;
d = dimension;
S = new float[n][d];
Best = new float[d];
UL_bound = new float[2][d];
//default bounds
for(int i = 0 ; i < d ; i++)
{
UL_bound[0][i] = -10000;
UL_bound[1][i] = 10000;
}
loopCounter = 0;
myRand = new Random();
Q = new float[n];
for(int i = 0 ; i < n ; i++)
Q[i] = 0;
V = new float[n][d];
for(int i = 0 ; i < n ; i++)
for(int j = 0 ; j < d ; j++)
V[i][j] = 0;
}
public void intial()
{
Sol = new float[n][d];
for(int i = 0 ; i < n ; i++)
for(int j = 0 ; j < d ; j++)
{
float t = myRand.nextFloat();
//(upper -lower)*rand + lower
Sol[i][j] = t * (UL_bound[1][j] - UL_bound[0][j]) + UL_bound[0][j];
}
fitness = new float[n];
for(int i = 0 ; i < n ; i++)
fitness[i] = function(Sol[i]);
//finding fmin
fmin = fitness[0];
fminIndex = 0;
for(int i = 0 ; i < n ; i++)
{
if (fitness[i] < fmin)
{
fmin = fitness[i];
fminIndex = i;
}
}
//setting best
for(int j = 0 ; j < d ; j++)
Best[j] = Sol[fminIndex][j];
}
public void start()
{
while(loopCounter < NofGen)
{
for(int i = 0 ; i < n ; i++)
{
Q[i] = Qmin + (Qmin - Qmax)* myRand.nextFloat();
for(int j = 0 ; j < d ; j++)
V[i][j] = V[i][j] + (Sol[i][j]-Best[j])*Q[i];
for(int j = 0 ; j < d ; j++)
S[i][j] = Sol[i][j] + V[i][j];
Sol[i] = simpleBounds(Sol[i]);
if(myRand.nextFloat() > r)
for(int j = 0 ; j < d ; j++)
S[i][j] = (float) (Best[j] + (.001 * myRand.nextFloat()) );
Fnew = function(S[i]);
if(Fnew <= fitness[i] && myRand.nextFloat() < A)
{
for(int j = 0 ; j < d ; j++)
Sol[i][j] = S[i][j];
fitness[i] = Fnew;
}
if(Fnew <= fmin)
{
fmin = Fnew;
for(int j = 0 ; j < d ; j++)
Best[j] = S[i][j];
}
}
loopCounter++;
}
}
public float[] simpleBounds(float p[])
{
for(int i = 0 ; i < d ; i++)
{
if(p[i] < UL_bound[0][i])
p[i] = UL_bound[0][i];
if(p[i] > UL_bound[1][i])
p[i] = UL_bound[1][i];
}
return p;
}
float function(float p[])
{
// Sphere function with fmin=0 at (0,0,...,0)
float sum = 0;
for(int i = 0 ; i < p.length ; i++)
sum = sum + p[i]*p[i];
return sum;
}
public float printResult()
{
System.out.println("After " + loopCounter + "Repeats :");
for(int i = 0 ; i < d ; i++)
System.out.print(Best[i] + ", ");
System.out.println ( "F(x) = " + fmin);
return fmin;
}
public void set_UL_Bound(int n, float L, float U)
{
if( n < d && n >= 0)
{
UL_bound[0][n] = L;
UL_bound[1][n] = U;
}
}
}
这是matlab版本
function [best,fmin,N_iter]=bat_algorithm(para)
% Display help
help bat_algorithm.m
% Default parameters
if nargin<1, para=[20 1000 0.5 0.5]; end
n=para(1); % Population size, typically 10 to 40
N_gen=para(2); % Number of generations
A=para(3); % Loudness (constant or decreasing)
r=para(4); % Pulse rate (constant or decreasing)
% This frequency range determines the scalings
% You should change these values if necessary
Qmin=0; % Frequency minimum
Qmax=2; % Frequency maximum
% Iteration parameters
N_iter=0; % Total number of function evaluations
% Dimension of the search variables
d=5; % Number of dimensions
% Lower limit/bounds/ a vector
Lb=-3*ones(1,d);
% Upper limit/bounds/ a vector
Ub=6*ones(1,d);
% Initializing arrays
Q=zeros(n,1); % Frequency
v=zeros(n,d); % Velocities
% Initialize the population/solutions
for i=1:n,
Sol(i,:)=Lb+(Ub-Lb).*rand(1,d);
Fitness(i)=Fun(Sol(i,:));
end
% Find the initial best solution
[fmin,I]=min(Fitness);
best=Sol(I,:);
for t=1:N_gen,
% Loop over all bats/solutions
for i=1:n,
Q(i)=Qmin+(Qmin-Qmax)*rand;
v(i,:)=v(i,:)+(Sol(i,:)-best)*Q(i);
S(i,:)=Sol(i,:)+v(i,:);
% Apply simple bounds/limits
Sol(i,:)=simplebounds(Sol(i,:),Lb,Ub);
% Pulse rate
if rand>r
% The factor 0.001 limits the step sizes of random walks
S(i,:)=best+0.001*randn(1,d);
end
% Evaluate new solutions
Fnew=Fun(S(i,:));
% Update if the solution improves, or not too loud
if (Fnew<=Fitness(i)) & (rand<A) ,
Sol(i,:)=S(i,:);
Fitness(i)=Fnew;
end
% Update the current best solution
if Fnew<=fmin,
best=S(i,:);
fmin=Fnew;
end
end
N_iter=N_iter+n;
end
% 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;
I=ns_tmp<Lb;
ns_tmp(I)=Lb(I);
% Apply the upper bound vector
J=ns_tmp>Ub;
ns_tmp(J)=Ub(J);
% Update this new move
s=ns_tmp;
function z=Fun(u)
% Sphere function with fmin=0 at (0,0,...,0)
z=sum(u.^2);
%%%%% ============ end ====================================