Article ; Online: A novel particle swarm optimization based on hybrid-learning model.
Mathematical biosciences and engineering : MBE
2023 Volume 20, Issue 4, Page(s) 7056–7087
Abstract: The convergence speed and the diversity of the population plays a critical role in the performance of particle swarm optimization (PSO). In order to balance the trade-off between exploration and exploitation, a novel particle swarm optimization based on ... ...
| Abstract | The convergence speed and the diversity of the population plays a critical role in the performance of particle swarm optimization (PSO). In order to balance the trade-off between exploration and exploitation, a novel particle swarm optimization based on the hybrid learning model (PSO-HLM) is proposed. In the early iteration stage, PSO-HLM updates the velocity of the particle based on the hybrid learning model, which can improve the convergence speed. At the end of the iteration, PSO-HLM employs a multi-pools fusion strategy to mutate the newly generated particles, which can expand the population diversity, thus avoid PSO-HLM falling into a local optima. In order to understand the strengths and weaknesses of PSO-HLM, several experiments are carried out on 30 benchmark functions. Experimental results show that the performance of PSO-HLM is better than other the-state-of-the-art algorithms. |
|---|---|
| Language | English |
| Publishing date | 2023-05-09 |
| Publishing country | United States |
| Document type | Journal Article |
| ZDB-ID | 2265126-3 |
| ISSN | 1551-0018 ; 1551-0018 |
| ISSN (online) | 1551-0018 |
| ISSN | 1551-0018 |
| DOI | 10.3934/mbe.2023305 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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