{'fraction_non_alphanumeric': 0.017094017094017096, 'fraction_numerical': 0.0, 'mean_word_length': 5.259541984732825, '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 Population-Based Algorithm for the k-Clustering Minimum Biclique Completion Problem

In this paper we propose a population-based method for tackling the k-clustering minimum bi-clique completion problem. We investigate the use of the discrete particle swarm optimisation combined with a special neighborhood decent procedure. Often configuration generated by the so-called swarm process induces infeasible solutions. In order to overcome to these situations, we introduce a tolerance strategy, where its aim is to highlight the quality of the current solution where an enlarging search space is considered. The behavior of the designed method is evaluated on some benchmark instances and its provided bounds are compared to those achieved by more recent methods available in the literature. New bounds are discovered.