Scholarship at UWindsor

Title

Applying student psychology-based optimization algorithm to optimize the performance of a thermoelectric generator

Authors

Xi Wang, University of Windsor
Paul Henshaw, University of Windsor
David S.K. Ting, University of Windsor

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