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DHHCO inherits and develops HHO's offspring updating mechanism, and meanwhile exerts the prediction ability of Kriging, reduces the number of expensive function evaluations, and provides new ideas for data-driven constraint optimization. Comprehensive experiments have been conducted on 13 benchmark functions and a real-world expensive optimization problem. The experimental results suggest that the proposed DHHCO can achieve quite competitive performance compared with six representative algorithms and can find the near global optimum with 200 function evaluations for most examples. Moreover, DHHCO is applied to the structural optimization of the internal components of the real underwater vehicle, and the final satisfactory weight reduction effect is more than 18%.<\/jats:p>","DOI":"10.1007\/s40747-022-00923-2","type":"journal-article","created":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T14:06:42Z","timestamp":1672063602000},"page":"4089-4110","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Data-driven Harris Hawks constrained optimization for computationally expensive constrained problems"],"prefix":"10.1007","volume":"9","author":[{"given":"Chongbo","family":"Fu","sequence":"first","affiliation":[]},{"given":"Huachao","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yihong","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,26]]},"reference":[{"key":"923_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106934","volume":"99","author":"H Dong","year":"2021","unstructured":"Dong H, Wang P, Yu X, Song B (2021) Surrogate-assisted teaching-learning-based optimization for high-dimensional and computationally expensive problems. 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