ArticleScientific reports2023
A novel hermit crab optimization algorithm.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Retrospection dropout bare-bones particle swarm optimization for feature-based brain tumor classification in MRI images.Frontiers in oncology · 2026Article
- A Novel Snow Leopard Optimization for High-Dimensional Feature Selection Problems.Sensors (Basel, Switzerland) · 2024Article
- Review
- A novel breast cancer image classification model based on multiscale texture feature analysis and dynamic learning.Scientific reports · 2024Article
- Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm.Scientific reports · 2024Article
- A cosine adaptive particle swarm optimization based long-short term memory method for urban green area prediction.PeerJ. Computer science · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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.
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Registered trials
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