Title
Applying student psychology-based optimization algorithm to optimize the performance of a thermoelectric generator
Document Type
Article
Publication Date
1-1-2023
Publication Title
International Journal of Green Energy
Keywords
mutation, particle swarm optimization, power optimization, SPBO, TEG
Abstract
In this study, two algorithms, the student psychology-based optimization (SPBO) and the mutation/particle swarm optimization (MPSO/PSO) were invoked to optimize the output power of a TEG module. The results indicate that compared to the MPSO/PSO, the SPBO has a better global searching ability under a low population size, while involving fewer adjustable parameters. The original SPBO algorithm was modified by introducing a mutation (M-SPBO). Then, the global searching ability, convergency, and repeatability of the M-SPBO were evaluated. The mutation subprogram improved the global searching ability of the original SPBO. With a high population size, the repeatability was also improved.
DOI
10.1080/15435075.2023.2194395
ISSN
15435075
E-ISSN
15435083
Recommended Citation
Wang, Xi; Henshaw, Paul; and Ting, David S.K.. (2023). Applying student psychology-based optimization algorithm to optimize the performance of a thermoelectric generator. International Journal of Green Energy.
https://scholar.uwindsor.ca/mechanicalengpub/52
