Task offloading and resource scheduling are fundamental challenges in EdgeCloud computing, where computational tasks generated by resource-constrained mobile and edge devices must be efficiently allocated to heterogeneous computing resources. Inefficient task allocation can result in increased makespan, communication cost, execution time, and underutilization of available computing resources. This research study presents a comparative evaluation of three standalone metaheuristic optimization algorithms, namely Bee Colony Optimization (BCO), Bacterial Foraging Optimization (BFO), and Grey Wolf Optimization (GWO), for task offloading and resource scheduling in EdgeCloud environments. Unlike hybrid optimization approaches, each algorithm is independently implemented and evaluated under identical experimental conditions to ensure a fair comparison. The algorithms are integrated into the CloudEdgeSim simulation framework, where EdgeCloud resources, mobile devices, base stations, cloudlets, hosts, virtual machines, and network characteristics are modeled. The experimental configuration considers 5 data centers, 10 hosts, 50 cloudlet virtual machines, and 100 offloading tasks, with defined computational, memory, bandwidth, and network parameters. The performance of BCO, BFO, and GWO is evaluated using four key criteria: resource utilization, makespan, communication cost, and execution time. Multiple workload scenarios and repeated simulation runs are considered to assess algorithmic stability, scalability, and convergence behavior. The comparative analysis identifies the strengths and limitations of each standalone optimization technique and determines their relative suitability for task offloading and resource scheduling in EdgeCloud environments. The findings provide a controlled experimental basis for selecting an appropriate standalone metaheuristic algorithm for efficient EdgeCloud resource management.