Hybrid Multi-objective Harris-Hawks and Moth-Flame Optimization Algorithm for Efficient Task Offloading strategy in IoT-Based Fog Computing Applications
Saranya M D, P. Pabitha · 2024
In the domain of Internet of Things (IoT)-based fog computing, efficient task offloading strategies play a pivotal role in optimizing resource utilization and enhancing system performance. This work presents a novel approach, the Hybrid Multi-objective Harris-Hawks and Moth-Flame Optimization Algorithm (HMHMFOA), for addressing the task offloading optimization challenge in fog computing environments. The proposed approach combines the strengths of the Harris-Hawks Optimization (HHO) and Moth-Flame Optimization (MFO) algorithms to strike a balance between exploration and exploitation in the search space, resulting in faster convergence and higher-quality solutions. The hybridization method is precisely engineered to capitalize on the complementary characteristics of the two algorithms, resulting in improved optimization performance. Experimental evaluations of IoT-based fog computing scenarios show that the HMHMFOA outperforms baseline algorithms in terms of numerous optimization targets, such as latency minimization, energy efficiency, and resource usage. The results demonstrate the efficacy of the proposed approach in supporting efficient task offloading decision-making, hence contributing to the advancement of fog computing applications. This study paves the way for future research into hybrid optimization strategies geared to IoT-centric computing paradigms.