ArticleArtificial intelligence review2023
Gaussian bare-bone slime mould algorithm: performance optimization and case studies on truss structures.
Article in Artificial intelligence review, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Gaussian bare‑bone JAYA algorithm for multi-threshold medical image segmentation.Scientific reports · 2025Article
- Advances in Slime Mould Algorithm: A Comprehensive Survey.Biomimetics (Basel, Switzerland) · 2024Review
- Advanced slime mould algorithm incorporating differential evolution and Powell mechanism for engineering design.iScience · 2023Article
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5 authors.
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Abstract
The slime mould algorithm (SMA) is a new meta-heuristic algorithm recently proposed. The algorithm is inspired by the foraging behavior of polycephalus slime moulds. It simulates the behavior and morphological changes of slime moulds during foraging through adaptive weights. Although the original SMA's performance is better than most swarm intelligence algorithms, it still has shortcomings, such as quickly falling into local optimal values and insufficient exploitation. This paper proposes a Gaussian barebone mutation enhanced SMA (GBSMA) to alleviate the original SMA's shortcomings. First of all, the Gaussian function in the Gaussian barebone accelerates the convergence while also expanding the search space, which improves the algorithm exploration and exploitation capabilities. Secondly, the differential evolution (DE) update strategy in the Gaussian barebone, using Supplementary Information: The online version contains supplementary material available at 10.1007/s10462-022-10370-7.
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