Several scenarios that use the Internet of Drones (IoD) networks require a Fog paradigm, where the Fog devices, provide time-sensitive functionality such as task allocation, scheduling, and resource optimization. The problem of efficient task allocation/scheduling is critical for optimizing Fog-enabled Internet of Drones performance. In recent years, many articles have employed meta-heuristic approaches for task scheduling/allocation in Fog-enabled IoT-based scenarios, focusing on network usage and delay, but neglecting execution time. While promising in the academic area, metaheuristic have many limitations in real-time environments due to their high execution time, resource-intensive nature, increased time complexity, and inherent uncertainty in achieving optimal solutions, as supported by empirical studies, case studies, and benchmarking data. We propose a task allocation method named F-DTA that is used as the fitness function of two metaheuristic approaches: Particle Swarm Optimization (PSO) and The Krill Herd Algorithm (KHA). We compare our proposed method by simulation using the iFogSim2 simulator, keeping all the settings the same for a fair evaluation and only focus on the execution time. The results confirm its superior performance in execution time, compared to the metaheuristics.