Recently, minimizing energy consumption in UAV-enabled IoT data collection (UIDC) systems by optimizing UAV deployment has attracted significant attention due to its crucial role in various applications such as smart cities, precision agriculture, and disaster response. Numerous optimization algorithms have been developed recently for this problem; however, they still struggle with slow convergence and suboptimal results. Therefore, in this study, three recent metaheuristic algorithms—the spider wasp optimizer (SWO), the gradient-based optimizer (GBO), and differential evolution (DE)—are adapted using the recently proposed optimized population size (oPS)-based encoding mechanism to present new variants, namely SSWoPS, SGBoPS, and SDEoPS, capable of minimizing the overall energy consumption (EC) of the UIDC system. This mechanism is improved by replacing stop points sequentially instead of randomly. This improvement preserves the algorithm’s capacity to explore and exploit during optimization, significantly decreasing the likelihood of getting stuck in local optima and accelerating convergence. The proposed algorithms are tested and validated at small, medium, and large scales using sixteen instances with several Internet of Things devices (IoTDs) ranging from 60 to 1100. They are compared against about thirteen competing algorithms across various performance metrics to highlight their superiority. According to the experimental results, SGBoPS outperforms all comparable algorithms in most instances, followed by SSWoPS and SDEoPS, indicating that the enhanced oPS-based mechanism can help optimization algorithms achieve outstanding results when applied to minimize the EC of the UIDC system.