I came across this code of firefly algorithm and need to use it in my major. If any of you could explain the working of the functions, it would be of great help to me. Thanks in advance.

```
#include<iostream>
#include<stdio.h>
#include<stdlib.h>
#include<math.h>
#include<time.h>
#include<string.h>
#include<memory.h>
#defineDUMP 1
#defineMAX_FFA 1000
#defineMAX_D 1000
usingnamespacestd;
int D = 1000; // dimension of the problem
int n = 20; // number of fireflies
intMaxGeneration; // number of iterations
intNumEval; // number of evaluations
int Index[MAX_FFA]; // sort of fireflies according to fitness values
doubleffa[MAX_FFA][MAX_D]; // firefly agents
doubleffa_tmp[MAX_FFA][MAX_D]; // intermediate population
double f[MAX_FFA]; // fitness values
double I[MAX_FFA]; // light intensity
doublenbest[MAX_FFA]; // the best solution found so far
doublelb[MAX_D]; // upper bound
doubleub[MAX_D]; // lower bound
double alpha = 0.5; // alpha parameter
doublebetamin = 0.2; // beta parameter
doublegama = 1.0; // gamma parameter
doublefbest; // the best objective function
typedefdouble (*FunctionCallback)(double sol[MAX_D]);
/*benchmark functions */
doublecost(double sol[MAX_D]);
doublesphere(double sol[MAX_D]);
/*Write your own objective function */
FunctionCallback function = &cost;
// optionally recalculate the new alpha value
doublealpha_new(double alpha, intNGen)
{
double delta; // delta parameter
delta = 1.0-pow((pow(10.0, -4.0)/0.9), 1.0/(double) NGen);
return (1-delta)*alpha;
}
// initialize the firefly population
voidinit_ffa()
{
inti, j;
double r;
// initialize upper and lower bounds
for (i=0;i<D;i++)
{
lb[i] = 0.0;
ub[i] = 2.0;
}
for (i=0;i<n;i++)
{
for (j=0;j<D;j++)
{
r = ( (double)rand() / ((double)(RAND_MAX)+(double)(1)) );
ffa[i][j]=r*(ub[i]-lb[i])+lb[i];
}
f[i] = 1.0; // initialize attractiveness
I[i] = f[i];
}
}
// implementation of bubble sort
voidsort_ffa()
{
inti, j;
// initialization of indexes
for(i=0;i<n;i++)
Index[i] = i;
// Bubble sort
for(i=0;i<n-1;i++)
{
for(j=i+1;j<n;j++)
{
if(I[i] > I[j])
{
double z = I[i]; // exchange attractiveness
I[i] = I[j];
I[j] = z;
z = f[i]; // exchange fitness
f[i] = f[j];
f[j] = z;
int k = Index[i]; // exchange indexes
Index[i] = Index[j];
Index[j] = k;
}
}
}
}
// replace the old population according the new Index values
voidreplace_ffa()
{
inti, j;
// copy original population to temporary area
for(i=0;i<n;i++)
{
for(j=0;j<D;j++)
{
ffa_tmp[i][j] = ffa[i][j];
}
}
// generational selection in sense of EA
for(i=0;i<n;i++)
{
for(j=0;j<D;j++)
{
ffa[i][j] = ffa_tmp[Index[i]][j];
}
}
}
voidfindlimits(int k)
{
inti;
for(i=0;i<D;i++)
{
if(ffa[k][i] <lb[i])
ffa[k][i] = lb[i];
if(ffa[k][i] >ub[i])
ffa[k][i] = ub[i];
}
}
voidmove_ffa()
{
inti, j, k;
double scale;
double r, beta;
for(i=0;i<n;i++)
{
scale = abs(ub[i]-lb[i]);
for(j=0;j<n;j++)
{
r = 0.0;
for(k=0;k<D;k++)
{
r += (ffa[i][k]-ffa[j][k])*(ffa[i][k]-ffa[j][k]);
}
r = sqrt(r);
if(I[i] > I[j]) // brighter and more attractive
{
double beta0 = 1.0;
beta = (beta0-betamin)*exp(-gama*pow(r, 2.0))+betamin;
for(k=0;k<D;k++)
{
r = ( (double)rand() / ((double)(RAND_MAX)+(double)(1)) );
doubletmpf = alpha*(r-0.5)*scale;
ffa[i][k] = ffa[i][k]*(1.0-beta)+ffa_tmp[j][k]*beta+tmpf;
}
}
}
findlimits(i);
}
}
voiddump_ffa(int gen)
{
cout<<"Dump at gen= "<< gen <<" best= "<<fbest<<endl;
}
/* display syntax messages */
voidhelp()
{
cout<<"Syntax:"<<endl;
cout<<" Firefly [-h|-?] [-l] [-p] [-c] [-k] [-s] [-t]"<<endl;
cout<<" Parameters: -h|-? = command syntax"<<endl;
cout<<" -n = number of fireflies"<<endl;
cout<<" -d = problem dimension"<<endl;
cout<<" -g = number of generations"<<endl;
cout<<" -a = alpha parameter"<<endl;
cout<<" -b = beta0 parameter"<<endl;
cout<<" -c = gamma parameter"<<endl;
}
intmain(intargc, char* argv[])
{
inti;
int t = 1; // generation counter
// interactive parameters handling
for(inti=1;i<argc;i++)
{
if((strncmp(argv[i], "-h", 2) == 0) || (strncmp(argv[i], "-?", 2) == 0))
{
help();
return0;
}
elseif(strncmp(argv[i], "-n", 2) == 0) // number of fireflies
{
n = atoi(&argv[i][2]);
}
elseif(strncmp(argv[i], "-d", 2) == 0) // problem dimension
{
D = atoi(&argv[i][2]);
}
elseif(strncmp(argv[i], "-g", 2) == 0) // number of generations
{
MaxGeneration = atoi(&argv[i][2]);
}
elseif(strncmp(argv[i], "-a", 2) == 0) // alpha parameter
{
alpha = atof(&argv[i][2]);
}
elseif(strncmp(argv[i], "-b", 2) == 0) // beta parameter
{
betamin = atof(&argv[i][2]);
}
elseif(strncmp(argv[i], "-c", 2) == 0) // gamma parameter
{
gama = atof(&argv[i][2]);
}
else
{
cerr<<"Fatal error: invalid parameter: "<<argv[i] <<endl;
return -1;
}
}
// firefly algorithm optimization loop
// determine the starting point of random generator
srand(1);
// generating the initial locations of n fireflies
init_ffa();
#ifdef DUMP
dump_ffa(t);
#endif
while(t <= MaxGeneration)
{
// this line of reducing alpha is optional
alpha = alpha_new(alpha, MaxGeneration);
// evaluate new solutions
for(i=0;i<n;i++)
{
f[i] = function(ffa[i]); // obtain fitness of solution
I[i] = f[i]; // initialize attractiveness
}
// ranking fireflies by their light intensity
sort_ffa();
// replace old population
replace_ffa();
// find the current best
for(i=0;i<D;i++)
nbest[i] = ffa[0][i];
fbest = I[0];
// move all fireflies to the better locations
move_ffa();
#ifdef DUMP
dump_ffa(t);
#endif
t++;
}
cout<<"End of optimization: fbest = "<<fbest<<endl;
return0;
}
// FF test function
doublecost(double* sol)
{
double sum = 0.0;
for(inti=0;i<D;i++)
sum += (sol[i]-1)*(sol[i]-1);
return sum;
}
doublesphere(double* sol) {
int j;
double top = 0;
for (j = 0; j < D; j++) {
top = top + sol[j] * sol[j];
}
return top;
}
```