## 1 Introduction
In the context of SAH optimization, [32] provides a good review, where various optimization algorithms such as GA, PSO (Particle Swarm Optimization), ABC, TLBO (Teaching-Learning-Based Optimization), and some of their variants are applied to SAH's optimization with respect to various input, design, and operating parameters, as well as types of SAH's. In [33], the PSO algorithm is applied to optimize the thermal efficiency of a glossy flat plate SAH by considering various design parameters, including the Reynolds number. In [34], PSO and TLBO are comparatively studied to optimize the thermohydraulic efficiency of roughened SAH's with twisted rib roughness. The study concludes that the TLBO approach performs better than the PSO in terms of the operating parameters considered with the experiment data. In [35], multi-objective PSO is used to optimize both the Nusselt number and thermal efficiency for three-sided roughened SAH's. In [36], ABC and GA are comparatively studied to optimize the thermal performance of a flat plate SAH. The study shows that the ABC algorithm is slightly superior to the GA under the specified operating parameters. Recently, in [37], the grey wolf optimization (GWO) and the dragonfly (DA) algorithm were applied to optimize the performance of solar air's heat's with a hemispherical protrusion roughness in a V-not's pattern on an absorber plate. The study optimizes the Nusselt number and friction factor under the specified operating environment and concludes that the GWO shows better performance than the DA for both objectives, with a faster computational rate and less variation. Recently, HFO-1 [24] hasbeen proposed as one of the swarm optimizations, which shows great success on diverse benchmark problems. It is particularly recommended by the authors in the cases where the problem dimension is small to medium. From the works in the literature, the following research gaps are observed:  
1. Very few swarm optimization algorithms have been used for the parametric optimization of solar air heaters involving multiple roughness geometries. 2. No comparison study has been reported for the parametric optimization of solar air heaters involving square, semicircular, and triangular ribs with the swarm and non-swarm optimization method. 3. No study has been reported using the HFO-1 optimization algorithms for the parametric optimization of solar air heaters. 4. The need for a better correlation model to predict the CFD data due to the cost of numerical simulation. In this paper, to address these research gaps, the parametric optimization of solar air heaters involving multiple roughness geometries (square, semicircular, and triangular ribs) are addressed with the novel Nusselt correlation models. Thus, a new correlation model predicting the Nusselt number is proposed using the HFO-1 swarm optimization algorithm to enhance corrugated solar air heaters. The paper also demonstrates a comparative study between the achievement of proposed models using both swarm (HFO-1) and non-swarm optimization, namely Generalized Reduced Gradient (GRG) optimization. For this purpose, A 2D numerical analysis was conducted using ANSYS Fluent 2019R3, a popular software for computational fluid dynamics (CFD), across a range of Reynolds numbers (4000-18,000) and for three levels of relative roughness. The main stages of this study are presented in Figure 1, where CFD data are used by the two optimization algorithms (HFO-1 and GRG) to obtain the optimized parameters of the proposed models and their performances are evaluated in terms of various metrics.