To perform such tests we must decide on a metric that encapsulates the performance of the code. For a global optimiser there are a few options available to us: energy difference between global minimum and lowest energy structure produced, number of unique low energy structures produced, lowest average energy past a certain number of iterations, number of iterations to reach below an energy threshold, energetic distribution of produced structures, etc. Assessing based on the lowest energy value would appear to be the natural choice to assess the criteria of a global optimiser. However, there are a few drawbacks to such a method. One such drawback is often we do not know what the global minimum should be, so the only comparison can be drawn from previous iterations of the methodology. Furthermore, in making this your target metric you will narrow your assessment down to an individual data point resulting a total loss of all other information about the quality of alternative structures produced. Alternatively, comparing the full energy distribution of different runs allows us to make some initial analysis on algorithm performance without needing to know the global minimum. Regardless of assessment choice, an average of values should be taken as stochastic methods can get 'lucky' or 'unlucky' for individual runs.