{'fraction_non_alphanumeric': 0.0182370820668693, 'fraction_numerical': 0.0, 'mean_word_length': 5.543046357615894, '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}}
A Hybrid Multi-group Co-evolution Intelligent Optimization Algorithm: PSO-GWO

A single swarm intelligence algorithm will be limited by its own search ability, and when dealing with some more complex matters, there will be problems such as low accuracy of solution and easy to fall into local optimum. However, the hybrid optimization method can achieve the purpose of improving the performance of the algorithm through information exchange between different populations. Therefore, in this paper, the particle swarm algorithm has the characteristics of individual memory, and the particle position update is used to replace the gray wolf individual position update, so that the gray wolf algorithm has memorability in the optimization. It can be seen from the optimization results of eight benchmark test functions that the "Multiply Species Cooperative Evolution" method combines the advantages of different algorithms, and it has obvious global convergence and strong competitiveness.