Firefly algorithm: How to understand the movement formula - algorithm

Question

The standard move formula for the firefly algorithm looks like this:
x_i^{t+1} = x_i^t + \beta_0 e^{-\gamma {r_{i,j}^2}}(x_j^t - x_i^t) + \alpha \epsilon_i^t
While I understand the idea of the algorithm and also what the single components of the formula are supposed to do, i have trouble transforming the formula into a working implementation.
To be specific:
1) beta0 should be the "attractiveness at the source", so when moving firefly i towards firefly j, a higher beta0 means moving ffi farther towards ffj.
But when performing minimization, higher fitness values indicate a worse solution, thus ffi should move less towards ffj.
So I guess, beta0 should be a value from 0 to 1, closer to 1 for better solutions. So how do I map minimization fitness values (including negative fitness values) to a 0-1 scale?
2) Does every firefly move towards every other firefly, or does every firefly only move towards the brightest one it sees? Most papers suggest two nested loops over all population members, but this also implies moving towards worse solutions (of course, beta0 could minimize the movement toward such bad solutions)
3) If you move every firefly towards every other firefly, do you add the random value every time, or only once per firefly and iteration?
Basically, i want the following snippet of code to do the movement:
void MoveFireFlies(double alpha, double gamma, double*** rand, double** NewPopulation, double** Population, double* Fitness, int populationSize, int dimensions)
{
int i;
for(i=0;i<populationSize;i++)
{
int j;
for(j=0; j<populationSize; j++)
{
double beta0 = 1.0;
double distance = [...] //euclidian distance between ffi and ffj
double factor = exp(-gamma * distance * distance);
int d;
for(d=0;d<dimensions;d++)
{
NewPopulation[i][d] = Population[i][d] + beta0 * factor * (Population[j][d] - Population[i][d]) + alpha * rand[i][j][d];
}
}
}
}
where rand contains random values in the range (-0.5,0.5), and Population contains the solutions of the previous iteration. The Fitness values are provided but currently not used anywhere. Of course, this codesnippet does not perform any kind of viable optimization at the moment.
Any help is greatly appreciated, I am working on this for quite some time and start to despair.
a paper concerning the firefly algorithm:
Firefly Algorithm: Recent Advances and Applications

Answer 1

First, there is difference between light intensity (brightness) and attractiveness. Light intensity is calculated with objective function. If the problem is about founding maximum global optimum, then you have to make light intensity directly proportional to objective function's value. Otherwise, light intensity must be inversly proportional to objective function's value.
On the other hand, attractiveness is not related to fitness value. Beta0 is not considired as fitness, Beta0 is a parameter that must be defined at the beginning as well as Alpha and Gamma parameters.
In case you have a minimization problem and you consider that objective function as a fitness value, then the firefly with higher fitness value will attract to less one.
Yes, every firefly moves towards every other firefly, but when you get a new solution, you have to perform a greedy selection between old and new one and keep only the best among them.
Yes, you have to generate a new random value everytime.