Abstract
Multi-Agent System (MAS) gained significant interest amongst researchers since it provides multiple benefits through several application areas. MAS involves a network of socially-cooperative smart agents that is conscious about the drastic modifications that occur in the platform at the time of task execution. On the other hand, energy efficiency is a major issue in real-time IoT systems, since most of the sensor nodes experience energy constraints. Though several works have been conducted earlier, there is a need exists to design an effective solution for simultaneous processing in real-time environments using multiple agents. The aim of Multi-Agent Pathfinding (MAPF) process is to provide collision-free routes so as to divert the agents from original path to the destination. In this view, the current study designs a Quasi-Oppositional Wild Horse Optimization-based Multi-Agent Path Finding (QOWHO-MAPF) scheme for real-time IoT systems. The aim of the proposed QOWHO-MAPF scheme is to determine the optimal set of paths to reach the destination in real-time IoT networks. QOWHO algorithm is created by integrating the concepts of Quasi-Oppositional Based Learning (QOBL) and conventional WHO algorithm. In addition, the proposed QOWHO-MAPF model derives a fitness function that involves two input parameters such as residual energy and distance-to-destination. The proposed QOWHO-MAPF model was experimentally analysed and the results were inspected under several aspects. The simulation results established that QOWHO-MAPF model is a superior model compared to other state-of-the-art models.