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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Veröffentlicht in:Results in engineering Jg. 23; S. 102833
Hauptverfasser: Ponnalagu, Dharswini, Ahmad, Mohd Ashraf, Jui, Julakha Jahan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.09.2024
Elsevier
Schlagworte:
Metaheuristics algorithms
Parameter identification
Slime mould algorithm
Thermoelectric modules
Thermoelectric modules
Metaheuristics algorithms
Parameter identification
Slime mould algorithm
ISSN:2590-1230, 2590-1230
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Zusammenfassung: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.
ISSN:2590-1230
2590-1230
DOI:10.1016/j.rineng.2024.102833