# Q：java版的蝙蝠算法在MATLAB

I have a Matlab code of bat algorithm and I write java version of this algorithm Bat algorithm is a simple optimization algorithm for finding the minimum of any function here is the matlab code and my java version of this code

My java version of this algorithm can't find the optimum result like matlab version and I can't find where is my mistake in converting the code from matlab to java

Can anyone help me where is my mistake?

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;
}
}

}

and this is the matlab versian

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 ====================================

I have a Matlab code of bat algorithm and I write java version of this algorithm Bat algorithm is a simple optimization algorithm for finding the minimum of any function here is the matlab code and my java version of this code

My java version of this algorithm can't find the optimum result like matlab version and I can't find where is my mistake in converting the code from matlab to java

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++)
[我]的[ J ] =（浮动）（最佳[ 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;
}
}

}

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
（我的，：）=最佳+ 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 ====================================
• The diff between two codes

In Matlab code:

S(i,:)=best+0.001*randn(1,d);

randn=>standard normal distribution.

While in Java code:

S[i][j] = (float) (Best[j] + (.001 * myRand.nextFloat()) );

java.util.Random.nextFloat()=>uniformly distributed float value between 0.0 and 1.0.

• The diff between two codes

（我的，：）=最佳+ 0.001 * randn（1，d）；

randn = & gt；标准正态分布。

[我]的[ J ] =（浮动）（最佳[ J ] +（001×myrand。nextfloat()））；

java util。随机的。nextfloat() = & gt；均匀分布的浮动值0和1之间。

this will be my first time here at stack overflow so i will say sorry beforehand if my response will be a bit ambiguous and has many problems. i just hope that this answer of mine will help future visitors on this thread who wants to study bat algo via java.

anyway, i did look at your code since i am studying bat algorithm at the moment. tried running it and it does gives far off results compared to the matlab version.

what i noticed is that you just "literally" tried to convert the matlab code without fully understanding each matlab lines. i wanted to point out all of the stuff you missed but i am feeling lazy right now so i will just leave my version of bat algorithm in java.

NOTE: i just made a running bat algorithm in java. not an efficient, fully debugged, matlab's java-equivalent bat algorithm.

import java.util.Random;

public class Mybat {

/**
* @param args the command line arguments
*/
public int n, N_gen, Qmin, Qmax, N_iter, d;
public double A,r,min,max,fnew,fmin;
public double Lb[][],Ub[][],Q[],v[][],Sol[][],S[][],fitness[],temp[],best[];
public Random random;
public static void main(String[] args) {
Mybat x = new Mybat(20,1000,0.5,0.5,5);

}

public Mybat(
int bats,
int generations,
double loud,
double pulse,
int dimension
){

//initialization of variables
n=bats;
N_gen = generations;
A = loud;
r = pulse;
d = dimension;

Random rand = new Random();

//plan to change later and added as parameter
min = -15;
max = 15;

fmin = 0;
//decleration for the bounds
Lb = new double[1][d];
Ub = new double[1][d];

Q = new double[n]; // frequency
v = new double[n][d]; //velocity

//initialize solution array
Sol = new double[n][d];
S = new double[n][d];
fitness = new double[n]; // fitness container
best =new double[d];
temp = new double[d];  //temporary holder for a row in array Sol

for(int i=0;i<n;i++){

// for minimal coding : added initialize Q[]array with '0' as element
Q[i] = 0;

for(int x=0;x<d;x++){

// for minimal coding : added initialize v[][] array with '0' as element
v[i][x] = 0;

//find random double values from LB to UB
Sol[i][x]= (rand.nextDouble()*(max - min)) + min;
temp[x] = Sol[i][x];
//System.out.println("sol["+i+"]["+x+"] = "+Sol[i][x]); //test line
//System.out.println(rand.nextDouble()); //test line
}
fitness[i] = function(temp);

//initialize best and the fmin
if(i==0 || fmin > fitness[i]){
fmin = fitness[i];

for(int x=0;x<d;x++){
best[x] = Sol[i][x];
}
}
System.out.println("fitness["+i+"]"+fitness[i]); //test
}
System.out.println("fmin = "+fmin); //test

// special note to these variables (below)
// change if required for maximum effectivity
Qmin = 0;
Qmax = 2;
N_iter = 1; //number of function evaluation

// bat proper
for(int loop=0;loop<N_iter;loop++){

// loop over all bats/solutions
for(int i=0;i<n;i++){
Q[i] = Qmin+((Qmin-Qmax)*rand.nextDouble());

// loop for velocity
for(int vel=0;vel<d;vel++){
v[i][vel] = v[i][vel]+((Sol[i][vel]-best[vel])*Q[i]);
}

//new solutions
for(int x=0;x<d;x++){
S[i][x] = Sol[i][x] + v[i][x];
}

/**
* RESERVED SPOT for the QUESTIONABLE AREA ON THE
* MATLAB CODE (i think it is not needed for the java equivalent)
*/

// pulse rate
if(rand.nextDouble()>r){
for(int x=0;x<d;x++){
S[i][x] = best[x]+(0.001*rand.nextGaussian());
}
}

//putting current row of Sol to a temp array
for(int x=0;x<d;x++){
temp[x] = S[i][x];
}
fnew = function(temp);

// update if solution is improved, and not too loud
if((fnew<=fitness[i]) && (rand.nextDouble()<A)){
for(int x=0;x<d;x++){
Sol[i][x] = S[i][x];
fitness[i] = fnew;
}
}

//update current best solution
if(fnew<=fmin){
for(int x=0;x<d;x++){
best[x] = S[i][x];
fmin = fnew;
}
}
}

}

System.out.println(" ");
System.out.println("new fitness");
for(int i=0;i<n;i++){

System.out.println("fitness["+i+"]"+fitness[i]);
}
System.out.println("Fmin = "+fmin);
}

//possible that this function is not needed in java
public void set_bounds(int x, double L, double U){
//double temp_Lb[x];
//double temp_Ub[x];
for(int i=0; i<x; i++){
Lb[0][i] = L;
Ub[0][i] = U;
}

}

public double function(double value[]){
// sphere function where fmin is at 0
double result = 0;

for(int i=0;i<d;i++){
result += (value[i]*value[i]);
}
return result;
}

}

anyway, i did look at your code since i am studying bat algorithm at the moment. tried running it and it does gives far off results compared to the matlab version.

import java.util.Random;

public class Mybat {

/**
* @param args the command line arguments
*/
public int n, N_gen, Qmin, Qmax, N_iter, d;
public double A,r,min,max,fnew,fmin;
public double Lb[][],Ub[][],Q[],v[][],Sol[][],S[][],fitness[],temp[],best[];
public Random random;
public static void main(String[] args) {
Mybat x = new Mybat(20,1000,0.5,0.5,5);

}

public Mybat(
int bats,
int generations,
double loud,
double pulse,
int dimension
){

//initialization of variables
n=bats;
N_gen = generations;
A = loud;
r = pulse;
d = dimension;

Random rand = new Random();

//plan to change later and added as parameter
min = -15;
max = 15;

fmin = 0;
//decleration for the bounds
Lb = new double[1][d];
Ub = new double[1][d];

Q = new double[n]; // frequency
v = new double[n][d]; //velocity

//initialize solution array
Sol = new double[n][d];
S = new double[n][d];
fitness = new double[n]; // fitness container
best =new double[d];
temp = new double[d];  //temporary holder for a row in array Sol

for(int i=0;i<n;i++){

// for minimal coding : added initialize Q[]array with '0' as element
Q[i] = 0;

for(int x=0;x<d;x++){

// for minimal coding : added initialize v[][] array with '0' as element
v[i][x] = 0;

//find random double values from LB to UB
Sol[i][x]= (rand.nextDouble()*(max - min)) + min;
temp[x] = Sol[i][x];
//System.out.println("sol["+i+"]["+x+"] = "+Sol[i][x]); //test line
//System.out.println(rand.nextDouble()); //test line
}
fitness[i] = function(temp);

//initialize best and the fmin
if(i==0 || fmin > fitness[i]){
fmin = fitness[i];

for(int x=0;x<d;x++){
best[x] = Sol[i][x];
}
}
System.out.println("fitness["+i+"]"+fitness[i]); //test
}
System.out.println("fmin = "+fmin); //test

// special note to these variables (below)
// change if required for maximum effectivity
Qmin = 0;
Qmax = 2;
N_iter = 1; //number of function evaluation

// bat proper
for(int loop=0;loop<N_iter;loop++){

// loop over all bats/solutions
for(int i=0;i<n;i++){
Q[i] = Qmin+((Qmin-Qmax)*rand.nextDouble());

// loop for velocity
for(int vel=0;vel<d;vel++){
v[i][vel] = v[i][vel]+((Sol[i][vel]-best[vel])*Q[i]);
}

//new solutions
for(int x=0;x<d;x++){
S[i][x] = Sol[i][x] + v[i][x];
}

/**
* RESERVED SPOT for the QUESTIONABLE AREA ON THE
* MATLAB CODE (i think it is not needed for the java equivalent)
*/

// pulse rate
if(rand.nextDouble()>r){
for(int x=0;x<d;x++){
S[i][x] = best[x]+(0.001*rand.nextGaussian());
}
}

//putting current row of Sol to a temp array
for(int x=0;x<d;x++){
temp[x] = S[i][x];
}
fnew = function(temp);

// update if solution is improved, and not too loud
if((fnew<=fitness[i]) && (rand.nextDouble()<A)){
for(int x=0;x<d;x++){
Sol[i][x] = S[i][x];
fitness[i] = fnew;
}
}

//update current best solution
if(fnew<=fmin){
for(int x=0;x<d;x++){
best[x] = S[i][x];
fmin = fnew;
}
}
}

}

System.out.println(" ");
System.out.println("new fitness");
for(int i=0;i<n;i++){

System.out.println("fitness["+i+"]"+fitness[i]);
}
System.out.println("Fmin = "+fmin);
}

//possible that this function is not needed in java
public void set_bounds(int x, double L, double U){
//double temp_Lb[x];
//double temp_Ub[x];
for(int i=0; i<x; i++){
Lb[0][i] = L;
Ub[0][i] = U;
}

}

public double function(double value[]){
// sphere function where fmin is at 0
double result = 0;

for(int i=0;i<d;i++){
result += (value[i]*value[i]);
}
return result;
}

}

I was looking for the solution in C# and stumbled up on this. It was enough to get the job done. Here is the solution in C# translated from the java with variables renamed and an additional fitness function for finding the solution of two x,y equations xy=6 and x+y = 5. Also included is finding the square root of .3 :

using System;

namespace BatAlgorithmC

namespace BatAlgorithmC
{
class Program
{
static void Main(string[] args)
{
//     Mybat x = new Mybat(100, 1000, 0.5, 0.5, 5, Mybat.sphere);
//      Mybat x = new Mybat(1000, 1000, 0.5, 0.5, 1, Mybat.squareRoot);

Mybat x = new Mybat(1000, 1000, 0.5, 0.5, 2, Mybat.RootOfXYEquations);
Console.WriteLine("Hit any key to continue.");

}

}

public class Mybat
{
/**
* @param args the command line arguments
*/
public int _numberOfBats, _generations, Qmin, Qmax, N_iter, _dimension;
public double _volume, _pulseRate, min, max, fnew, fmin;
public double[][] _lowerBound, _upperBound, _velocity, _solution, S;
public double[] _fitness, _tempSolution, _bestSolution, Q;
public Random random;
//public static void main(String[] args) {
//   Mybat x = new Mybat(20,1000,0.5,0.5,5, Mybat.sphere);

//}

public static void initJagged(double[][] array, int n, int d)
{
for (int i = 0; i < n; i++) array[i] = new double[d];
}

public Mybat(
int bats,
int generations,
double loud,
double pulse,
int dimension,
Func<double[], int, double> function
)
{
//initialization of variables
_numberOfBats = bats;
_generations = generations;
_volume = loud;
_pulseRate = pulse;
_dimension = dimension;

Random random = new Random();

//plan to change later and added as parameter
min = -15;
max = 15;

fmin = 0;
//decleration for the bounds
_lowerBound = new double[1][];
_upperBound = new double[1][];

Q = new double[_numberOfBats]; // frequency
_velocity = new double[_numberOfBats][]; //velocity

initJagged(_velocity, _numberOfBats, _dimension);
initJagged(_lowerBound, 1, _dimension);
initJagged(_upperBound, 1, _dimension);

//initialize solution array
_solution = new double[_numberOfBats][];
S = new double[_numberOfBats][];
_fitness = new double[_numberOfBats]; // fitness container
_bestSolution = new double[_dimension];
_tempSolution = new double[_dimension]; //temporary holder for a row in array _solution

initJagged(_solution, _numberOfBats, _dimension);
initJagged(S, _numberOfBats, _dimension);

for (int i = 0; i < _numberOfBats; i++)
{
// for minimal coding : added initialize Q[]array with '0' as element
Q[i] = 0;

for (int x = 0; x < _dimension; x++)
{
// for minimal coding : added initialize _velocity[][] array with '0' as element
_velocity[i][x] = 0;

//find random double values from LB to UB
_solution[i][x] = (random.NextDouble()*(max - min)) + min;
_tempSolution[x] = _solution[i][x];
//Console.WriteLine("sol["+i+"]["+x+"] = "+_solution[i][x]); //test line
//Console.WriteLine(rand.nextDouble()); //test line
}
_fitness[i] = function(_tempSolution, _dimension);

//initialize best and the fmin
if (i == 0 || fmin > _fitness[i])
{
fmin = _fitness[i];

for (int x = 0; x < _dimension; x++)
{
_bestSolution[x] = _solution[i][x];
}
}
Console.WriteLine("fitness[" + i + "]" + _fitness[i]); //test
}
Console.WriteLine("fmin = " + fmin); //test

// special note to these variables (below)
// change if required for maximum effectivity
Qmin = 0;
Qmax = 2;
N_iter = 1; //number of function evaluation

// bat proper
for (int loop = 0; loop < N_iter; loop++)
{
// loop over all bats/solutions
for (int nextBat = 0; nextBat < _numberOfBats; nextBat++)
{
Q[nextBat] = Qmin + ((Qmin - Qmax)*random.NextDouble());

// loop for velocity
for (int vel = 0; vel < _dimension; vel++)
{
_velocity[nextBat][vel] = _velocity[nextBat][vel] +
((_solution[nextBat][vel] - _bestSolution[vel])*Q[nextBat]);
}

//new solutions
for (int nextDimension = 0; nextDimension < _dimension; nextDimension++)
{
S[nextBat][nextDimension] = _solution[nextBat][nextDimension] +
_velocity[nextBat][nextDimension];
}

/**
* RESERVED SPOT for the QUESTIONABLE AREA ON THE
* MATLAB CODE (i think it is not needed for the java equivalent)
*/

// pulse rate
if (random.NextDouble() > _pulseRate)
{
for (int nextDimension = 0; nextDimension < _dimension; nextDimension++)
{
S[nextBat][nextDimension] = _bestSolution[nextDimension] + (0.001*random.NextGaussian());
}
}

//putting current row of _solution to a temp array
for (int nextDimension = 0; nextDimension < _dimension; nextDimension++)
{
_tempSolution[nextDimension] = S[nextBat][nextDimension];
}
fnew = function(_tempSolution, _dimension);

// update if solution is improved, and not too loud
if ((fnew <= _fitness[nextBat]) && (random.NextDouble() < _volume))
{
for (int x = 0; x < _dimension; x++)
{
_solution[nextBat][x] = S[nextBat][x];
_fitness[nextBat] = fnew;
}
}

//update current best solution
if (fnew <= fmin)
{
for (int nextDimension = 0; nextDimension < _dimension; nextDimension++)
{
_bestSolution[nextDimension] = S[nextBat][nextDimension];
fmin = fnew;
}
}
}
}

Console.WriteLine(" ");
Console.WriteLine("new fitness");
for (int i = 0; i < _numberOfBats; i++)
{
Console.WriteLine("fitness[" + i + "]" + _fitness[i]);
}

for (int nextDimension = 0; nextDimension < _dimension; nextDimension++)
{
Console.WriteLine("best[" + nextDimension + "]" + _bestSolution[nextDimension]);
}
Console.WriteLine("Fmin = " + fmin);
}

//possible that this function is not needed in java
public void set_bounds(int x, double L, double U)
{
//double temp_Lb[x];
//double temp_Ub[x];
for (int i = 0; i < x; i++)
{
_lowerBound[0][i] = L;
_upperBound[0][i] = U;
}
}

public static double sphere(double[] value, int d)
{
// sphere function where fmin is at 0
double result = 0;

for (int i = 0; i < d; i++)
{
result += (value[i]*value[i]);
}
return result;
}

public static double squareRoot(double[] value, int d)
{
// find the square root of .3
double result = 0;

for (int i = 0; i < d; i++)
{
result += Math.Abs(.3 - (value[i]*value[i]));
}
return result;
}

public static double RootOfXYEquations(double[] value, int d)
{
// solve for x and y  xy = 6  and x+y = 5
double result = 0;

result += Math.Abs(5 - (value[0] + value[1]));
result += Math.Abs(6 - (value[0] * value[1]));

return result;
}
}

static class MathExtensiionns
{
public static double NextGaussian(this Random rand)
{
double u1 = rand.NextDouble(); //these are uniform(0,1) random doubles
double u2 = rand.NextDouble();
double mean = 0, stdDev = 1;
double randStdNormal = Math.Sqrt(-2.0 * Math.Log(u1)) *
Math.Sin(2.0 * Math.PI * u2); //random normal(0,1)
double randNormal =
mean + stdDev * randStdNormal; //random normal(mean,stdDev^2)
return randNormal;
}
}
}

using System;

namespace BatAlgorithmC

namespace BatAlgorithmC
{
class Program
{
static void Main(string[] args)
{
//     Mybat x = new Mybat(100, 1000, 0.5, 0.5, 5, Mybat.sphere);
//      Mybat x = new Mybat(1000, 1000, 0.5, 0.5, 1, Mybat.squareRoot);

Mybat x = new Mybat(1000, 1000, 0.5, 0.5, 2, Mybat.RootOfXYEquations);
Console.WriteLine("Hit any key to continue.");

}

}

public class Mybat
{
/**
* @param args the command line arguments
*/
public int _numberOfBats, _generations, Qmin, Qmax, N_iter, _dimension;
public double _volume, _pulseRate, min, max, fnew, fmin;
public double[][] _lowerBound, _upperBound, _velocity, _solution, S;
public double[] _fitness, _tempSolution, _bestSolution, Q;
public Random random;
//public static void main(String[] args) {
//   Mybat x = new Mybat(20,1000,0.5,0.5,5, Mybat.sphere);

//}

public static void initJagged(double[][] array, int n, int d)
{
for (int i = 0; i < n; i++) array[i] = new double[d];
}

public Mybat(
int bats,
int generations,
double loud,
double pulse,
int dimension,
Func<double[], int, double> function
)
{
//initialization of variables
_numberOfBats = bats;
_generations = generations;
_volume = loud;
_pulseRate = pulse;
_dimension = dimension;

Random random = new Random();

//plan to change later and added as parameter
min = -15;
max = 15;

fmin = 0;
//decleration for the bounds
_lowerBound = new double[1][];
_upperBound = new double[1][];

Q = new double[_numberOfBats]; // frequency
_velocity = new double[_numberOfBats][]; //velocity

initJagged(_velocity, _numberOfBats, _dimension);
initJagged(_lowerBound, 1, _dimension);
initJagged(_upperBound, 1, _dimension);

//initialize solution array
_solution = new double[_numberOfBats][];
S = new double[_numberOfBats][];
_fitness = new double[_numberOfBats]; // fitness container
_bestSolution = new double[_dimension];
_tempSolution = new double[_dimension]; //temporary holder for a row in array _solution

initJagged(_solution, _numberOfBats, _dimension);
initJagged(S, _numberOfBats, _dimension);

for (int i = 0; i < _numberOfBats; i++)
{
// for minimal coding : added initialize Q[]array with '0' as element
Q[i] = 0;

for (int x = 0; x < _dimension; x++)
{
// for minimal coding : added initialize _velocity[][] array with '0' as element
_velocity[i][x] = 0;

//find random double values from LB to UB
_solution[i][x] = (random.NextDouble()*(max - min)) + min;
_tempSolution[x] = _solution[i][x];
//Console.WriteLine("sol["+i+"]["+x+"] = "+_solution[i][x]); //test line
//Console.WriteLine(rand.nextDouble()); //test line
}
_fitness[i] = function(_tempSolution, _dimension);

//initialize best and the fmin
if (i == 0 || fmin > _fitness[i])
{
fmin = _fitness[i];

for (int x = 0; x < _dimension; x++)
{
_bestSolution[x] = _solution[i][x];
}
}
Console.WriteLine("fitness[" + i + "]" + _fitness[i]); //test
}
Console.WriteLine("fmin = " + fmin); //test

// special note to these variables (below)
// change if required for maximum effectivity
Qmin = 0;
Qmax = 2;
N_iter = 1; //number of function evaluation

// bat proper
for (int loop = 0; loop < N_iter; loop++)
{
// loop over all bats/solutions
for (int nextBat = 0; nextBat < _numberOfBats; nextBat++)
{
Q[nextBat] = Qmin + ((Qmin - Qmax)*random.NextDouble());

// loop for velocity
for (int vel = 0; vel < _dimension; vel++)
{
_velocity[nextBat][vel] = _velocity[nextBat][vel] +
((_solution[nextBat][vel] - _bestSolution[vel])*Q[nextBat]);
}

//new solutions
for (int nextDimension = 0; nextDimension < _dimension; nextDimension++)
{
S[nextBat][nextDimension] = _solution[nextBat][nextDimension] +
_velocity[nextBat][nextDimension];
}

/**
* RESERVED SPOT for the QUESTIONABLE AREA ON THE
* MATLAB CODE (i think it is not needed for the java equivalent)
*/

// pulse rate
if (random.NextDouble() > _pulseRate)
{
for (int nextDimension = 0; nextDimension < _dimension; nextDimension++)
{
S[nextBat][nextDimension] = _bestSolution[nextDimension] + (0.001*random.NextGaussian());
}
}

//putting current row of _solution to a temp array
for (int nextDimension = 0; nextDimension < _dimension; nextDimension++)
{
_tempSolution[nextDimension] = S[nextBat][nextDimension];
}
fnew = function(_tempSolution, _dimension);

// update if solution is improved, and not too loud
if ((fnew <= _fitness[nextBat]) && (random.NextDouble() < _volume))
{
for (int x = 0; x < _dimension; x++)
{
_solution[nextBat][x] = S[nextBat][x];
_fitness[nextBat] = fnew;
}
}

//update current best solution
if (fnew <= fmin)
{
for (int nextDimension = 0; nextDimension < _dimension; nextDimension++)
{
_bestSolution[nextDimension] = S[nextBat][nextDimension];
fmin = fnew;
}
}
}
}

Console.WriteLine(" ");
Console.WriteLine("new fitness");
for (int i = 0; i < _numberOfBats; i++)
{
Console.WriteLine("fitness[" + i + "]" + _fitness[i]);
}

for (int nextDimension = 0; nextDimension < _dimension; nextDimension++)
{
Console.WriteLine("best[" + nextDimension + "]" + _bestSolution[nextDimension]);
}
Console.WriteLine("Fmin = " + fmin);
}

//possible that this function is not needed in java
public void set_bounds(int x, double L, double U)
{
//double temp_Lb[x];
//double temp_Ub[x];
for (int i = 0; i < x; i++)
{
_lowerBound[0][i] = L;
_upperBound[0][i] = U;
}
}

public static double sphere(double[] value, int d)
{
// sphere function where fmin is at 0
double result = 0;

for (int i = 0; i < d; i++)
{
result += (value[i]*value[i]);
}
return result;
}

public static double squareRoot(double[] value, int d)
{
// find the square root of .3
double result = 0;

for (int i = 0; i < d; i++)
{
result += Math.Abs(.3 - (value[i]*value[i]));
}
return result;
}

public static double RootOfXYEquations(double[] value, int d)
{
// solve for x and y  xy = 6  and x+y = 5
double result = 0;

result += Math.Abs(5 - (value[0] + value[1]));
result += Math.Abs(6 - (value[0] * value[1]));

return result;
}
}

static class MathExtensiionns
{
public static double NextGaussian(this Random rand)
{
double u1 = rand.NextDouble(); //these are uniform(0,1) random doubles
double u2 = rand.NextDouble();
double mean = 0, stdDev = 1;
double randStdNormal = Math.Sqrt(-2.0 * Math.Log(u1)) *
Math.Sin(2.0 * Math.PI * u2); //random normal(0,1)
double randNormal =
mean + stdDev * randStdNormal; //random normal(mean,stdDev^2)
return randNormal;
}
}
}
java  algorithm  matlab  random