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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Main Authors: Ponnalagu, Dharswini, Mohd Ashraf, Ahmad, Jui, Julakha Jahan
Format: Article
Language:English
Published: Elsevier B.V. 2024
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/42792/1/Parameter%20identification%20of%20thermoelectric%20modules%20using%20enhanced%20slime%20mould%20algorithm.pdf
http://umpir.ump.edu.my/id/eprint/42792/
https://doi.org/10.1016/j.rineng.2024.102833
https://doi.org/10.1016/j.rineng.2024.102833
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spelling my.ump.umpir.427922024-10-18T03:42:01Z http://umpir.ump.edu.my/id/eprint/42792/ Parameter identification of thermoelectric modules using enhanced slime mould algorithm (ESMA) Ponnalagu, Dharswini Mohd Ashraf, Ahmad Jui, Julakha Jahan TK Electrical engineering. Electronics Nuclear engineering 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. Elsevier B.V. 2024-09-03 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/42792/1/Parameter%20identification%20of%20thermoelectric%20modules%20using%20enhanced%20slime%20mould%20algorithm.pdf Ponnalagu, Dharswini and Mohd Ashraf, Ahmad and Jui, Julakha Jahan (2024) Parameter identification of thermoelectric modules using enhanced slime mould algorithm (ESMA). Results in Engineering, 23 (102833). pp. 1-10. ISSN 2590-1230. (Published) https://doi.org/10.1016/j.rineng.2024.102833 https://doi.org/10.1016/j.rineng.2024.102833
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Ponnalagu, Dharswini
Mohd Ashraf, Ahmad
Jui, Julakha Jahan
Parameter identification of thermoelectric modules using enhanced slime mould algorithm (ESMA)
description 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.
format Article
author Ponnalagu, Dharswini
Mohd Ashraf, Ahmad
Jui, Julakha Jahan
author_facet Ponnalagu, Dharswini
Mohd Ashraf, Ahmad
Jui, Julakha Jahan
author_sort Ponnalagu, Dharswini
title Parameter identification of thermoelectric modules using enhanced slime mould algorithm (ESMA)
title_short Parameter identification of thermoelectric modules using enhanced slime mould algorithm (ESMA)
title_full Parameter identification of thermoelectric modules using enhanced slime mould algorithm (ESMA)
title_fullStr Parameter identification of thermoelectric modules using enhanced slime mould algorithm (ESMA)
title_full_unstemmed Parameter identification of thermoelectric modules using enhanced slime mould algorithm (ESMA)
title_sort parameter identification of thermoelectric modules using enhanced slime mould algorithm (esma)
publisher Elsevier B.V.
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/42792/1/Parameter%20identification%20of%20thermoelectric%20modules%20using%20enhanced%20slime%20mould%20algorithm.pdf
http://umpir.ump.edu.my/id/eprint/42792/
https://doi.org/10.1016/j.rineng.2024.102833
https://doi.org/10.1016/j.rineng.2024.102833
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score 13.23648