{'fraction_non_alphanumeric': 0.03118279569892473, 'fraction_numerical': 0.001075268817204301, 'mean_word_length': 5.510489510489511, '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 cloud-based artificial immune network for optimization

This paper proposes an artificial immune network based on the cloud model (AINet-CM) for complex optimization problems. By introducing the cloud model to evaluate the candidate antibodies, three major immune operators are redesigned to enhance the convergence performance of AINet-CM. These operators are the increasing half cloud-based cloning operator, the asymmetrical cloud-based mutation operator and the normal similarity cloud-based suppression, respectively. Also, a dynamic searching step length is considered. A series of numerical simulations are arranged for technical investigations and three artificial immune systems (i.e., opt-aiNet, IA-AIS and AAIS-2S) are selected for comparison with AINet-CM. The experimental results suggest that the proposed AINet-CM algorithm outperforms the other three immune algorithms in convergence speed and solution accuracy.