A novel hermit crab optimization algorithm
Abstract High-dimensional optimization has numerous potential applications in both academia and industry. It is a major challenge for optimization algorithms to generate very accurate solutions in high-dimensional search spaces. However, traditional search tools are prone to dimensional catastrophes...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-37129-6 |
| _version_ | 1797795717640617984 |
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| author | Jia Guo Guoyuan Zhou Ke Yan Binghua Shi Yi Di Yuji Sato |
| author_facet | Jia Guo Guoyuan Zhou Ke Yan Binghua Shi Yi Di Yuji Sato |
| author_sort | Jia Guo |
| collection | DOAJ |
| description | Abstract High-dimensional optimization has numerous potential applications in both academia and industry. It is a major challenge for optimization algorithms to generate very accurate solutions in high-dimensional search spaces. However, traditional search tools are prone to dimensional catastrophes and local optima, thus failing to provide high-precision results. To solve these problems, a novel hermit crab optimization algorithm (the HCOA) is introduced in this paper. Inspired by the group behaviour of hermit crabs, the HCOA combines the optimal search and historical path search to balance the depth and breadth searches. In the experimental section of the paper, the HCOA competes with 5 well-known metaheuristic algorithms in the CEC2017 benchmark functions, which contain 29 functions, with 23 of these ranking first. The state of work BPSO-CM is also chosen to compare with the HCOA, and the competition shows that the HCOA has a better performance in the 100-dimensional test of the CEC2017 benchmark functions. All the experimental results demonstrate that the HCOA presents highly accurate and robust results for high-dimensional optimization problems. |
| first_indexed | 2024-03-13T03:22:15Z |
| format | Article |
| id | doaj.art-794f0cf7ae7f4c9f88979d933f4014dc |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-13T03:22:15Z |
| publishDate | 2023-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-794f0cf7ae7f4c9f88979d933f4014dc2023-06-25T11:15:15ZengNature PortfolioScientific Reports2045-23222023-06-0113112610.1038/s41598-023-37129-6A novel hermit crab optimization algorithmJia Guo0Guoyuan Zhou1Ke Yan2Binghua Shi3Yi Di4Yuji Sato5School of Information Engineering, Hubei University of EconomicsSchool of Information Engineering, Hubei University of EconomicsChina Construction Third Engineering Bureau Installation Engineering Co., Ltd.School of Information Engineering, Hubei University of EconomicsSchool of Information Engineering, Hubei University of EconomicsFaculty of Computer and Information Sciences, Hosei UniversityAbstract High-dimensional optimization has numerous potential applications in both academia and industry. It is a major challenge for optimization algorithms to generate very accurate solutions in high-dimensional search spaces. However, traditional search tools are prone to dimensional catastrophes and local optima, thus failing to provide high-precision results. To solve these problems, a novel hermit crab optimization algorithm (the HCOA) is introduced in this paper. Inspired by the group behaviour of hermit crabs, the HCOA combines the optimal search and historical path search to balance the depth and breadth searches. In the experimental section of the paper, the HCOA competes with 5 well-known metaheuristic algorithms in the CEC2017 benchmark functions, which contain 29 functions, with 23 of these ranking first. The state of work BPSO-CM is also chosen to compare with the HCOA, and the competition shows that the HCOA has a better performance in the 100-dimensional test of the CEC2017 benchmark functions. All the experimental results demonstrate that the HCOA presents highly accurate and robust results for high-dimensional optimization problems.https://doi.org/10.1038/s41598-023-37129-6 |
| spellingShingle | Jia Guo Guoyuan Zhou Ke Yan Binghua Shi Yi Di Yuji Sato A novel hermit crab optimization algorithm Scientific Reports |
| title | A novel hermit crab optimization algorithm |
| title_full | A novel hermit crab optimization algorithm |
| title_fullStr | A novel hermit crab optimization algorithm |
| title_full_unstemmed | A novel hermit crab optimization algorithm |
| title_short | A novel hermit crab optimization algorithm |
| title_sort | novel hermit crab optimization algorithm |
| url | https://doi.org/10.1038/s41598-023-37129-6 |
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