A transition to solar energy systems is considered one of the most important alternatives to conventional fossil fuels. Until recently, solar air heaters (SAHs) were among the other solar energy systems that have been widely used in various households and industrial applications. However, the recent literature reveals that efficiencies of SAHs are still low. Some metaheuristic algorithms have been used to enhance the efficiencies of these SAH systems. In the paper, we do not only discuss the techniques used to enhance the performance of SAHs, but we also reviewed a majority of published papers on the applications of SAH optimization. The metaheuristic algorithms include simulated annealing (SA), particle swarm optimization (PSO), genetic algorithm (GA), artificial bee colony (ABC), teaching-learning-based optimization (TLBO), and elitist teaching-learning-based optimization (ETLBO). For this research, it should be noted that this study is mostly based on the literature published in the last ten years in good energy top journals. Therefore, this paper clearly shows that the use of all six proposed metaheuristic algorithms results in significant efficiency improvements through the selection of the optimal design set and operating parameters for SAHs. Based on the past literature and on the outcomes of this paper, ETLBO is unquestionably more competitive than ABC, GA, PSO, SA, and TLBO for the optimization of SAHs for the same considered problem. Finally, based on the covered six state-of-the-art metaheuristic techniques, some perspectives and recommendations for the future outlook of SAH optimization are proposed. This paper is the first-ever attempt to present the current developments to a large audience on the applications of metaheuristic methods in SAH optimization. Thus, researchers can use this paper for further research and for the advancement of the proposed and other recommended algorithms to generate the best performance for the various SAHs.

中文翻译：

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元启发式算法在太阳能空气加热器优化中的应用：近期趋势和未来展望

过渡到太阳能系统被认为是传统化石燃料的最重要替代品之一。直到最近，太阳能空气加热器（SAH）还是在各种家庭和工业应用中广泛使用的其他太阳能系统之一。但是，最近的文献显示SAH的效率仍然很低。一些元启发式算法已用于提高这些SAH系统的效率。在本文中，我们不仅讨论了用于增强SAH性能的技术，而且还回顾了有关SAH优化应用的大多数已发表论文。元启发式算法包括模拟退火（SA），粒子群优化（PSO），遗传算法（GA），人工蜂群（ABC），基于教学学习的优化（TLBO），以及基于精英教学学习的优化（ETLBO）。对于本研究，应注意的是，本研究主要基于近十年来在高能顶级期刊上发表的文献。因此，本文清楚地表明，通过选择SAH的最佳设计集和操作参数，使用全部六个拟议的元启发式算法可显着提高效率。根据过去的文献和本文的结果，对于相同的问题，在优化SAH方面，ETLBO无疑比ABC，GA，PSO，SA和TLBO更具竞争力。最后，基于涵盖的六种最新的元启发式技术，为SAH优化的未来展望提出了一些观点和建议。本文是有史以来第一次向大批读者介绍元启发式方法在SAH优化中的应用的最新进展。因此，研究人员可以将本文用于进一步的研究以及所提出的算法和其他推荐算法的发展，从而为各种SAH产生最佳性能。