{'fraction_non_alphanumeric': 0.02588686481303931, 'fraction_numerical': 0.0, 'mean_word_length': 5.6075949367088604, '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 variant of group search optimizer for global optimization

Group search optimizer (GSO) is a stochastic, population-based optimization technique that has shown better performance as for global searching when optimizing multimodal benchmarks. However, it suffers from poor convergence because of its producer-scrounger model, which makes it unable to get a reliable estimiator for evolution path. In order to enhance the local search ability without impairing the global seach ability of GSO, this paper proposes a variant of group search optimizer (VGSO), which develops a producer-organizer model whereby group members are assumed to search for opportunities of either ‘finding’ (producing) or ‘learning’ (organization). This model enables VGSO to achieve a good trade-off between its exploration and exploitation abilities. A comprehensive experimental study is conducted on a set of benchmark functions and a practical optimization problem. Comparison results show that VGSO obtains a promising performance on these optimization problems.