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Secondly, the equilibrium pooling technique is included to assist the standard honey badger algorithm (HBA) break free of the local optimum. Finally, the updated honey badger population individuals are updated with Levy flight strategy to produce more potential solutions. Ten famous benchmark test datasets are utilized to evaluate the efficiency of the DELHBA algorithm and to contrast it with twelve of the current most used swarm intelligence algorithms and k-means. Additionally, DELHBA algorithm\u2019s performance is assessed using the Wilcoxon rank sum test and Friedman\u2019s test. The experimental results show that DELHBA has better clustering accuracy, convergence speed and stability compared with other algorithms, demonstrating its superiority in solving clustering problems.<\/jats:p>","DOI":"10.3233\/jifs-231922","type":"journal-article","created":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T11:56:17Z","timestamp":1690286177000},"page":"5739-5763","source":"Crossref","is-referenced-by-count":0,"title":["An equilibrium honey badger algorithm with differential evolution strategy for cluster analysis"],"prefix":"10.1177","volume":"45","author":[{"given":"Peixin","family":"Huang","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence, Guangxi University for Nationalities, Nanning, China"}]},{"given":"Qifang","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Guangxi University for Nationalities, Nanning, China"},{"name":"Guangxi Key Laboratories of Hybrid Computation and IC Design 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