With the proliferation of Internet of Things (IoT) applications, a massive amount of data has been produced, requiring an efficient platform to store and process this data. Cloud computing has the ability to tackle such enormous data, but cannot provide real-time response to latency sensitive IoT applications. Fog computing delivers the cloud service at network edge with rapid response to IoT applications, but the computation offloading decision, unpredictable demands, unbalanced workload among fog servers and heterogeneity become challenging issues. Hence, this study applied an effective approach named Spider Monkey Optimization (SMO) to address the mentioned challenges and employs the proposed framework to ensure the Quality of Service (QoS) parameters performance. This framework uses an adaptive approach to allocate the workload based upon the computation ability of the resource, and continuously monitoring the workload among the fog nodes avoids the possibility of overloading and underloading the fog nodes. Exploration and exploitation ability of the SMO algorithm reduces the chances of being trapped in the local optimum and decides the optimal offloading destination. The performance of the proposed approach is assessed in a simulation environment, showing that the proposed algorithm reduces parameters such as latency, communication overhead, cost and energy consumption compared to baseline approaches Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA) using the same experimental environments.