{'fraction_non_alphanumeric': 0.018300653594771243, 'fraction_numerical': 0.0, 'mean_word_length': 5.491525423728813, '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 damping factor based particle swarm optimization approach

This paper proposes a novel damping factor based particle swarm optimization (DFPSO) to solve the large scale and high-dimensional searching space problems in terms of convergence to global optima. In this optimal searching strategy, we balance the exploring and exploiting ability of particles by introducing a new damping factor. Also, fuzzy c-means clustering is applied to cluster the particles' positions for the individuals' neighborhood establishment. Our comparative study about benchmark test functions demonstrates that the proposed variant of PSO outweighs the performance of standard PSO and three state-of-art variants of PSO in terms of global optimum convergence and final optimal results.