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\end{equation}  where $D(\textbf{c}_j,\textbf{x}_j)$ stands for the distance between centroid $\textbf{c}_j\in\Re^n$ and sample $\textbf{x}_i$, and $N$ denotes the number of dataset samples.   Roughly speaking, given a problem with $k$ clusters, each agent (e.g., harmonies, particles, bats or fireflies) encodes a possible solution in $\Re^{k*n}$, as depicted in Figure~\ref{f.problem_representation}. Therefore, after placing all agents with random positions, the $k$-means algorithm is executed once for each agent using that positions as the starting point.  \section{Methodology}