Data-driven Harris Hawks constrained optimization for computationally expensive constrained problems
Abstract Aiming at the constrained optimization problem where function evaluation is time-consuming, this paper proposed a novel algorithm called data-driven Harris Hawks constrained optimization (DHHCO). In DHHCO, Kriging models are utilized to prospect potentially optimal areas by leveraging compu...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2022-12-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-022-00923-2 |
| _version_ | 1827890154940923904 |
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| author | Chongbo Fu Huachao Dong Peng Wang Yihong Li |
| author_facet | Chongbo Fu Huachao Dong Peng Wang Yihong Li |
| author_sort | Chongbo Fu |
| collection | DOAJ |
| description | Abstract Aiming at the constrained optimization problem where function evaluation is time-consuming, this paper proposed a novel algorithm called data-driven Harris Hawks constrained optimization (DHHCO). In DHHCO, Kriging models are utilized to prospect potentially optimal areas by leveraging computationally expensive historical data during optimization. Three powerful strategies are, respectively, embedded into different phases of conventional Harris Hawks optimization (HHO) to generate diverse candidate sample data for exploiting around the existing sample data and exploring uncharted region. Moreover, a Kriging-based data-driven strategy composed of data-driven population construction and individual selection strategy is presented, which fully mines and utilizes the potential available information in the existing sample data. 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%. |
| first_indexed | 2024-03-12T21:05:39Z |
| format | Article |
| id | doaj.art-0ea77f0758bc4167805af2678fab6454 |
| institution | Directory Open Access Journal |
| issn | 2199-4536 2198-6053 |
| language | English |
| last_indexed | 2024-03-12T21:05:39Z |
| publishDate | 2022-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj.art-0ea77f0758bc4167805af2678fab64542023-07-30T11:28:04ZengSpringerComplex & Intelligent Systems2199-45362198-60532022-12-01944089411010.1007/s40747-022-00923-2Data-driven Harris Hawks constrained optimization for computationally expensive constrained problemsChongbo Fu0Huachao Dong1Peng Wang2Yihong Li3School of Marine Science and Technology, Northwestern Polytechnical UniversitySchool of Marine Science and Technology, Northwestern Polytechnical UniversitySchool of Marine Science and Technology, Northwestern Polytechnical UniversitySchool of Marine Science and Technology, Northwestern Polytechnical UniversityAbstract Aiming at the constrained optimization problem where function evaluation is time-consuming, this paper proposed a novel algorithm called data-driven Harris Hawks constrained optimization (DHHCO). In DHHCO, Kriging models are utilized to prospect potentially optimal areas by leveraging computationally expensive historical data during optimization. Three powerful strategies are, respectively, embedded into different phases of conventional Harris Hawks optimization (HHO) to generate diverse candidate sample data for exploiting around the existing sample data and exploring uncharted region. Moreover, a Kriging-based data-driven strategy composed of data-driven population construction and individual selection strategy is presented, which fully mines and utilizes the potential available information in the existing sample data. 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%.https://doi.org/10.1007/s40747-022-00923-2Data-driven optimizationKrigingComputationally expensiveHarris Hawks optimizationModel management |
| spellingShingle | Chongbo Fu Huachao Dong Peng Wang Yihong Li Data-driven Harris Hawks constrained optimization for computationally expensive constrained problems Complex & Intelligent Systems Data-driven optimization Kriging Computationally expensive Harris Hawks optimization Model management |
| title | Data-driven Harris Hawks constrained optimization for computationally expensive constrained problems |
| title_full | Data-driven Harris Hawks constrained optimization for computationally expensive constrained problems |
| title_fullStr | Data-driven Harris Hawks constrained optimization for computationally expensive constrained problems |
| title_full_unstemmed | Data-driven Harris Hawks constrained optimization for computationally expensive constrained problems |
| title_short | Data-driven Harris Hawks constrained optimization for computationally expensive constrained problems |
| title_sort | data driven harris hawks constrained optimization for computationally expensive constrained problems |
| topic | Data-driven optimization Kriging Computationally expensive Harris Hawks optimization Model management |
| url | https://doi.org/10.1007/s40747-022-00923-2 |
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