This method comes with the advantage of assessing the algorithms ability to find low-energy minima and not give up once a minimum has been found. Similarly to the previous plots pyGlobOpt without grouping performs strongly in the beginning but tapers off once the pool of structures onlooker and employed bees pull from has been poisoned with multiple similar structures. C 0 outperforms standard pyGlobOpt in 250 iterations and pyGlobOpt with grouping in just 75 iterations. The average lowest energy structures found after 1000 iterations is 0.23 eV above GM for standard pyGlobOpt , 0.17 eV for C 0 , and 0.08 eV for pyGlobOpt with clustering.