ArticlePloS one2022
A twinning bare bones particle swarm optimization algorithm.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Retrospection dropout bare-bones particle swarm optimization for feature-based brain tumor classification in MRI images.Frontiers in oncology · 2026Article
- Neural network prediction model based on Levy flight and natural biomimetic technology for its application in cancer prediction.PloS one · 2025Article
- A cosine adaptive particle swarm optimization based long-short term memory method for urban green area prediction.PeerJ. Computer science · 2024Article
- A novel hermit crab optimization algorithm.Scientific reports · 2023Article
- A deep memory bare-bones particle swarm optimization algorithm for single-objective optimization problems.PloS one · 2023Article
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7 authors.
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Abstract
A twinning bare bones particle swarm optimization(TBBPSO) algorithm is proposed in this paper. The TBBPSO is combined by two operators, the twins grouping operator (TGO) and the merger operator (MO). The TGO aims at the reorganization of the particle swarm. Two particles will form as a twin and influence each other in subsequent iterations. In a twin, one particle is designed to do the global search while the other one is designed to do the local search. The MO aims at merging the twins and enhancing the search ability of the main group. Two operators work together to enhance the local minimum escaping ability of proposed methods. In addition, no parameter adjustment is needed in TBBPSO, which means TBBPSO can solve different types of optimization problems without previous information or parameter adjustment. In the benchmark functions test, the CEC2014 benchmark functions are used. Experimental results prove that proposed methods can present high precision results for various types of optimization problems.
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