Quasi‐oppositional wild horse optimization based multi‐agent path finding scheme for real time IoT systems

Abstract

AbstractMulti‐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.