Multi-objective optimization of two stage thermoelectric cooler using a modified teaching–learning-based optimization algorithm

Teaching–learning-based optimization (TLBO) is a recently developed heuristic algorithm based on the natural phenomenon of teaching–learning process. In the present work, a modified version of the TLBO algorithm is introduced and applied for the multi-objective optimization of a two stage thermoelec...

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Veröffentlicht in:Engineering applications of artificial intelligence Jg. 26; H. 1; S. 430 - 445
Hauptverfasser: Venkata Rao, R., Patel, Vivek
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.01.2013
Schlagworte:
Coefficient of performance
Cooling capacity
Teaching–learning-based optimization
Thermoelectric modules
Two stage thermoelectric cooler
Thermoelectric modules
Cooling capacity
Teaching–learning-based optimization
Coefficient of performance
Two stage thermoelectric cooler
ISSN:0952-1976, 1873-6769
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Zusammenfassung:Teaching–learning-based optimization (TLBO) is a recently developed heuristic algorithm based on the natural phenomenon of teaching–learning process. In the present work, a modified version of the TLBO algorithm is introduced and applied for the multi-objective optimization of a two stage thermoelectric cooler (TEC). Two different arrangements of the thermoelectric cooler are considered for the optimization. Maximization of cooling capacity and coefficient of performance of the thermoelectric cooler are considered as the objective functions. An example is presented to demonstrate the effectiveness and accuracy of the proposed algorithm. The results of optimization obtained by using the modified TLBO are validated by comparing with those obtained by using the basic TLBO, genetic algorithm (GA), particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms. ► Basic TLBO algorithm is modified. ► Multi-objective optimization of a two stage thermoelectric cooler is considered. ► Maximization of cooling capacity and COP are the objective functions. ► The proposed optimization algorithm can be extended to other thermal systems.
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2012.02.016