{'fraction_non_alphanumeric': 0.01881720430107527, 'fraction_numerical': 0.0, 'mean_word_length': 5.422413793103448, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
The guaranteed convergence mayfly optimization algorithm

Although the updating equations of velocities for individuals in the mayfly optimization (MO) algorithm were not the same as that for individuals in the particle swarm optimization (PSO) algorithm, there was still a similarity among the equations. Therefore, when the swarms were approaching to the local or global optima, the individuals would be trapped in local optima in the same way. Consequently, the guaranteed convergence improvement should also be introduced to the MO algorithm. This work was reported in this paper, and simulation experiments were carried out. The results verified that the guaranteed convergence MO algorithm would also perform better than the original one.