An enhanced seagull optimization algorithm for multi-dimensional internet of things routing under heterogeneity and mobility constraints
TL;DR
Inspired by the migration and attack behaviors of seagulls, ESOA integrates collaborative subgrouping, simulated generation, and random rearrangement mechanisms to achieve an effective balance between exploration and exploitation and significantly improves network lifetime.
Abstract
The rapid growth of the Internet of Things (IoT) has created several complications for routing and parameter optimization, especially in heterogeneous networks characterized by mobility and large scale. Poor routing algorithms will result in energy waste, congestion, high latency, and unreliable packet delivery. As such, this limits the efficacy and viability of IoT for deploying applications such as smart cities and industrial automation. To address these challenges, this paper proposes an Enhanced Seagull Optimization Algorithm (ESOA) for multi-objective IoT routing optimization. Inspired by the migration and attack behaviors of seagulls, ESOA integrates collaborative subgrouping, simulated generation, and random rearrangement mechanisms to achieve an effective balance between exploration and exploitation. The proposed algorithm simultaneously optimizes critical network performance metrics, including energy consumption, traffic congestion, transmission delay, and packet loss. Extensive simulations in heterogeneous and mobility-aware scenarios demonstrate that ESOA significantly improves network lifetime, reduces routing cost, minimizes congestion and end-to-end delay, enhances packet delivery performance, and preserves higher residual energy than several state-of-the-art bio-inspired and metaheuristic optimization algorithms.