Fog computing has emerged as a revolutionary paradigm to serve massive data in the Internet of Things (IoT) environment. It is a derivative of cloud computing that provides cloud-like services at the edge of the network. Subsequently, it resolves the significant issue of higher delay faced in cloud-IoT paradigm. According to the literature, the inefficient scheduling of users tasks in fog computing may result in higher delays in comparison to cloud computing. Hence, the real benefits of fog computing can only be obtained by applying effective job scheduling strategies. In fact, task scheduling is an NP-hard problem that cannot be solved by any specific algorithm to reach an ideal solution. Hence, it requires the optimal and efficient techniques to cater to the issues of latency, response time and efficient resource utilization of the available fog resources at the edge of the network. Given this, we proposed a novel bioinspired hybrid algorithm (NBIHA) which is a hybrid of modified particle swarm optimization (MPSO) and modified cat swarm optimization (MCSO). In the proposed scheme, MPSO is used to schedule the tasks among fog devices and the hybrid of MPSO and MCSO is used to manage resources at the fog device level. In the proposed approach, the resources are assigned and managed on the basis of the demand of incoming requests. The main objective of the proposed work is to reduce the average response time and to optimize resource utilization by efficiently scheduling the tasks and managing available fog resources. The simulations are carried out using iFogSim. The evaluation results show that the proposed approach (NBIHA) shows promising results in terms of energy consumption, execution time and average response time in comparison to the state-of-the-art scheduling techniques.