In the recent past, huge amount of data is generated during processing in a Cloud Computing (CC) environment, resulting in a systematic increase in load. Internet of Things (IoT)-based applications in general necessitate the combination of both CC and Fog Computing (FC) to overcome the limitations of response time and latency. In specific, FC facilitates scheduling of tasks over different fog nodes in the network. However, as the resources of fog nodes are limited, achieving optimal task scheduling is identified to be more challenging. In this paper, Wild Horse Optimization Algorithm-based Energy-Efficient Task Scheduling (WHOAEETS) algorithm is proposed for achieving better response time and reduced latency during task execution in an FC environment. WHOAEETS algorithm schedules tasks on the fog server based on fitness evaluation parameters that include execution time, energy and makespan. Wild Horse Optimizer (WHO) is adopted for scheduling tasks to ideal fog servers by using search agents that explore and exploit factors of scheduling in search space. Simulation results of the proposed WHOAEETS algorithm confirm 8.32% minimized energy consumption, 9.61% reduced execution time and 11.42% minimized makespan in contrast to baseline approaches taken for evaluation.