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Multi-objective optimization of a Stirling heat engine using TS-TLBO (tutorial training and self learning inspired teaching-learning based optimization) algorithm
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DOI：10.1016/j.energy.2015.12.030.png)
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Abstract
En 中文
In the present work, TS-TLBO (tutorial training and self learning inspired teaching-learning-based optimization) algorithm is proposed and investigated for the multi-objective optimization of a Stirling heat engine. The exploration and exploitation capacity of the basic MO-TLBO (multi objective teaching learning-based optimization) is enhance by introducing the concept of tutorial training and self motivated learning. The multi-objective TS-TLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions maintained in an external archive. Optimization of a Stirling heat engine is carried out by considering two and three objective functions simultaneously for the maximization of thermal efficiency, output power and minimization of total pressure drop of the engine. Application examples are presented to demonstrate the effectiveness and accuracy of the proposed algorithm. (C) 2015 Elsevier Ltd. All rights reserved.
Keywords:
Multi-objective optimization
Stirling heat engine
Thermal efficiency
Output power
Total pressure drop
Tutorial training and self learning inspired teaching-learning-based optimization (TSTLBO)
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