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

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.

Improving Wild Horse Optimizer: Integrating Multistrategy for Robust Performance across Multiple Engineering Problems and Evaluation Benchmarks

“…The wild horse optimizer (WHO) is a successful metaheuristic algorithm, which has been recently proposed based on the social behavior of wild horses [26]. The WHO algorithm has been applied in power system frequency control [27], car parking lot location determination [28], photovoltaic model parameter extraction [29], Internet of Things system path finding [30], and automatic driving recognition calculation [31]. Although a stallion is selected to lead the team search in each iteration, which shows the strong exploitation ability of the stallion in WHO, it suffers from the problem of insufficient convergence accuracy and low exploration ability [32,33].…”

Special issue on International Conference on Computing and Communication Networks

“…Finally, Maray et al, 2022;Marzouk et al, 2022 We want to express our sincere thanks to the editor-in-chief for allowing us to organize this particular issue. The editorial office staffs are excellent, and we thank them for their support.…”

“…Marzouk et al, 2022 introduce 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.…”

“…Finally, Maray et al, 2022; Marzouk et al, 2022 introduce Intelligent Metaheuristics‐based Feature Selection model with Optimal ML approach for Malware Detection (IMFSOML‐MD) on IoT‐enabled MTS. Primarily, IMFSOML‐MD technique involves the design of Quantum Invasive Weed Optimization Algorithm‐based Feature Selection technique to optimally choose a subset of features.…”