A novel hermit crab optimization algorithm

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 loca...

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Bibliographic Details
Published in:Scientific reports 2023-06, Vol.13 (1), p.9934-9934, Article 9934
Main Authors: Guo, Jia, Zhou, Guoyuan, Yan, Ke, Shi, Binghua, Di, Yi, Sato, Yuji
Format: Article
Language:English
Subjects:
631/181
639/166
Algorithms
Crustaceans
Disasters
Humanities and Social Sciences
multidisciplinary
Optimization algorithms
Science
Science (multidisciplinary)
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Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-37129-6