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The proposed algorithm is applied to solve CEC2017 numerical problems, and its robustness is verified based on the simulation results. Moreover, OKHA is applied to tackle data clustering problems selected from the UCI Machine Learning Repository. The experimental results illustrate that OKHA is superior to or at least competitive with other representative clustering techniques.<\/jats:p>","DOI":"10.3233\/ida-195056","type":"journal-article","created":{"date-parts":[[2021,4,23]],"date-time":"2021-04-23T14:39:56Z","timestamp":1619188796000},"page":"605-626","source":"Crossref","is-referenced-by-count":2,"title":["A novel krill herd algorithm with orthogonality and its application to data clustering"],"prefix":"10.1177","volume":"25","author":[{"given":"Chen","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence, Nankai University, Tianjin, China"},{"name":"Key Laboratory of Intelligent Robotics of Tianjin, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongxin","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nankai University, Tianjin, China"},{"name":"Key Laboratory of Intelligent Robotics of Tianjin, 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