## 5 Conclusions  
This study aims to model, design, optimize, simulate, and track the GPP of a photovoltaic energy system under uniform and non-uniform (partial shading) conditions; both static and dynamic circumstances have been covered. The two metaheuristic MPPT algorithms, PSO and CSO, have been applied for tracking the GPP under static and dynamic uniform irradiance and partial shading. The results obtained proved that both the PSO and CSO techniques can follow the GPP efficiently and accurately under static uniform and partial shading circumstances. But they failed to follow the GPP under dynamic uniform and non-uniform circumstances. This means that these metaheuristic MPPT algorithms cannot deal with dynamic or time-variant conditions, whether under uniform or non-uniform irradiances. Although they performed well in tracking the GPP, the PSO takes more time to search for the GMP, and the oscillations of the PSO are higher than those of the CSO. Therefore, the CSO algorithm performed well in comparison with the PSO in terms of GPP tracking, tracking time, and oscillations around the maximum power. The main concluding remarks from this study can be summarized as follows:  
* In static uniform conditions, both CSO and PSO effectively tracked the single peak, with CSO demonstrating faster tracking than PSO. For example, CSO attained a PPV of 743.66 W (99.55% efficiency), outperforming PSO's 738.5 W (98.8% efficiency). Moreover, PSO showed greater oscillations around the GPP compared with CSO, indicating CSO's superior stability during steady-state operation. * Under static PSCs, the power-voltage curve displayed a single GPP alongside multiple local peaks. Both algorithms successfully tracked the GPP, but CSO surpassed PSO with quicker tracking and reduced oscillations. In this scenario, both achieved 636.9 W (99.9% efficiency), though CSO's faster convergence and lower oscillation profile were notable. * In dynamic uniform conditions, CSO and PSO initially tracked the GPP effectively, but they could not adjust after subsequent irradiance shifts. * In dynamic PSCs, both algorithms tracked the GPP at first, but they were unable to follow the new GPP following changes in irradiance. In summary, CSO and PSO are adept at handling static irradiance conditions, whether uniform or non-uniform (PSCs), successfully tracking the global power in scenarios with a single GPP (uniform conditions) or a GPP with multiple local peaks (static PSCs). They can effectively differentiate between global and local peaks, capturing the global one with precision. However, when conditions change dynamically over time, both CSO and PSO struggle to adapt to the new GPP, whether under uniform or PSC scenarios. This reveals their limitation in managing dynamic or time-varying conditions, whether uniform or PSCs, without specific enhancements. Such shortcomings lead to significant energy and efficiency losses, resulting in substantial time and cost impacts. We aim to address these challenges in future research by exploring potential solutions. Moreover, future work will include experimental validation on a commercial MCU, assessing real-time performance, memory usage, and control stability to ensure practical deployment in PV systems. Conceptualization, A.A.A.-S. and H.M.H.F. ; methodology, H.M.H.F. and A.A.A.-S; software, A.A.A.-S. and H.M.H.F. formal analysis, A.A.A.-S. and H.M.H.F., investigation H.M.H.F. ; resources, A.A.A.-S. and H.M.H.F. ; data curation, A.A.A.-S. and H.M.H.F. ; writing--original draft preparation, A.A.A.-S. and H.M.H.F. ; writing--review and editing, A.A.A.-S. and H.M.H.F. ; visualization, A.A.A.-S.; supervision, A.A.A.-S.; project administration, A.A.A.-S. and H.M.H.F. ; funding acquisition, A.A.A.-S. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant no. : IMSIU-DDRSP2503). **Institutional Review Board Statement:** Not applicable.