Hybrid Emperor Penguin and Gravitational Search Optimization for Efficient Task Co-Offloading in Fog Computing Environments (Statistics, Optimization & Information Computing, 2025)
The industrial fog computing setting is highly sensitive to challenges in efficiently scheduling tasks and offloading computing processes because of the heterogeneity of devices, their mobility, and fluctuations in resource availability. This work is motivated by the need to design a unified optimization mechanism for dynamic fog environments where both device and network heterogeneity severely affect task allocation. The proposed study aims to address the limitations of existing single-strategy offloading methods by developing a hybrid optimization framework that balances computational load, minimizes latency, and reduces energy consumption. The main contribution of this paper lies in introducing a hybrid GSA–EPO co-offloading model that achieves an adaptive trade-off between energy efficiency and service latency while maintaining scalability in large-scale fog computing systems. The hybridized framework integrates the Gravitational Search Algorithm (GSA) and Emperor Penguin Optimization (EPO) to reduce energy consumption, service latency, and penalties related to deadline violations. The model uses battery-aware willingness, memory constraints, and mobility-sensitive communication parameters to inform local execution, device-to-device offloading, and edge server offloading. The hybrid GSA–EPO algorithm combines the exploration capability of GSA with the convergence efficiency of EPO within a migration-based knowledge-exchange structure, thereby enhancing both convergence and diversity of solutions. Simulation results demonstrate that the proposed approach achieves up to 63% reduction in energy consumption  and 75% reduction in delay compared to baseline methods, confirming the feasibility of hybrid metaheuristic strategies for reliable and efficient task co-offloading in dynamic fog computing networks.