Parameter identification of thermoelectric modules using enhanced slime mould algorithm (ESMA)
This paper sets pioneering research which investigates the parametric identification of thermoelectric modules (TEMs) through the employment of enhanced slime mould algorithm (ESMA). The proposed method incorporates a pair of modifications to the standard slime mould algorithm (SMA). Primary modific...
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| Vydáno v: | Results in engineering Ročník 23; s. 102833 |
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Elsevier B.V
01.09.2024
Elsevier |
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| ISSN: | 2590-1230, 2590-1230 |
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| Abstract | This paper sets pioneering research which investigates the parametric identification of thermoelectric modules (TEMs) through the employment of enhanced slime mould algorithm (ESMA). The proposed method incorporates a pair of modifications to the standard slime mould algorithm (SMA). Primary modification encloses computation of random average position between the slimes' current individual position and best individual position towards resolution of local optima issue. Subsequent modification then involves substitution of an exponential function to the existing tangent hyperbolic function within formula p of the standard SMA in enabling improved probability variants via the selection of updated equations. Competency of the proposed algorithm in generating the optimal parameters for TEMs was appraised based on 21 benchmarked design parameters, following the objective of root mean square error (RMSE) minimization between the temperature of both actual and estimated models. Acquired results which demonstrate lower values of RMSE and parameter deviation index against the standard SMA and other preceding algorithms such as particle swarm optimization, sine cosine algorithm, moth flame optimizer and ant lion optimizer ultimately verified ESMA’s efficacy as an effective approach for accurate model identification.
•Random average position is proposed to solve the issue of local optima entrapment in the original SMA.•An exponential function in formula p of SMA give more variations of probability in the updated equation selection.•ESMA provides better accuracy model of TEMs than the original SMA. |
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| AbstractList | This paper sets pioneering research which investigates the parametric identification of thermoelectric modules (TEMs) through the employment of enhanced slime mould algorithm (ESMA). The proposed method incorporates a pair of modifications to the standard slime mould algorithm (SMA). Primary modification encloses computation of random average position between the slimes' current individual position and best individual position towards resolution of local optima issue. Subsequent modification then involves substitution of an exponential function to the existing tangent hyperbolic function within formula p of the standard SMA in enabling improved probability variants via the selection of updated equations. Competency of the proposed algorithm in generating the optimal parameters for TEMs was appraised based on 21 benchmarked design parameters, following the objective of root mean square error (RMSE) minimization between the temperature of both actual and estimated models. Acquired results which demonstrate lower values of RMSE and parameter deviation index against the standard SMA and other preceding algorithms such as particle swarm optimization, sine cosine algorithm, moth flame optimizer and ant lion optimizer ultimately verified ESMA’s efficacy as an effective approach for accurate model identification. This paper sets pioneering research which investigates the parametric identification of thermoelectric modules (TEMs) through the employment of enhanced slime mould algorithm (ESMA). The proposed method incorporates a pair of modifications to the standard slime mould algorithm (SMA). Primary modification encloses computation of random average position between the slimes' current individual position and best individual position towards resolution of local optima issue. Subsequent modification then involves substitution of an exponential function to the existing tangent hyperbolic function within formula p of the standard SMA in enabling improved probability variants via the selection of updated equations. Competency of the proposed algorithm in generating the optimal parameters for TEMs was appraised based on 21 benchmarked design parameters, following the objective of root mean square error (RMSE) minimization between the temperature of both actual and estimated models. Acquired results which demonstrate lower values of RMSE and parameter deviation index against the standard SMA and other preceding algorithms such as particle swarm optimization, sine cosine algorithm, moth flame optimizer and ant lion optimizer ultimately verified ESMA’s efficacy as an effective approach for accurate model identification. •Random average position is proposed to solve the issue of local optima entrapment in the original SMA.•An exponential function in formula p of SMA give more variations of probability in the updated equation selection.•ESMA provides better accuracy model of TEMs than the original SMA. |
| ArticleNumber | 102833 |
| Author | Jui, Julakha Jahan Ahmad, Mohd Ashraf Ponnalagu, Dharswini |
| Author_xml | – sequence: 1 givenname: Dharswini surname: Ponnalagu fullname: Ponnalagu, Dharswini email: dharswini97@gmail.com – sequence: 2 givenname: Mohd Ashraf orcidid: 0000-0002-2948-5123 surname: Ahmad fullname: Ahmad, Mohd Ashraf email: mashraf@umpsa.edu.my – sequence: 3 givenname: Julakha Jahan surname: Jui fullname: Jui, Julakha Jahan email: julakha.ump@gmail.com |
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| Cites_doi | 10.1109/JSEN.2007.894917 10.1007/s11664-020-07966-6 10.1007/s11664-012-2009-6 10.1016/j.asoc.2020.106642 10.1016/j.advengsoft.2013.12.007 10.1007/s00521-022-07277-3 10.1016/j.simpat.2017.11.004 10.1016/j.enconman.2015.12.003 10.1038/nnano.2011.39 10.1016/j.future.2020.03.055 10.1016/j.energy.2016.10.136 10.1016/j.jpowsour.2010.12.015 10.1016/j.rineng.2024.102506 10.1109/ACCESS.2022.3183627 10.1080/0952813X.2022.2040598 10.1016/j.renene.2023.05.100 10.1007/s10462-019-09732-5 10.1016/j.knosys.2015.07.006 10.3390/fractalfract7070561 10.1016/j.enconman.2021.114981 10.1109/ACCESS.2020.3018866 10.1016/j.advengsoft.2022.103185 10.3390/en13143742 10.1007/s11831-023-09883-3 10.1016/j.ijheatmasstransfer.2022.123718 10.1016/j.jpowsour.2013.06.081 10.1080/10407782.2019.1630241 10.1016/j.rineng.2023.100940 10.3138/flor.20.015 |
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| Keywords | Thermoelectric modules Metaheuristics algorithms Parameter identification Slime mould algorithm |
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