A Hierarchical Fractal Space NSGA-II-Based Cloud–Fog Collaborative Optimization Framework for Latency and Energy-Aware Task Offloading in Smart Manufacturing

Abstract

The growth of intelligent manufacturing systems has led to a wealth of computation-intensive tasks with complex dependencies. These tasks require an efficient offloading architecture that balances responsiveness and energy efficiency across distributed computing resources. Existing task offloading approaches have fundamental limitations when simultaneously optimizing multiple conflicting objectives while accommodating hierarchical computing architectures and heterogeneous resource capabilities. To address these challenges, this paper presents a cloud–fog hierarchical collaborative computing (CFHCC) framework that features fog cluster mechanisms. These methods enable coordinated, multi-node parallel processing while maintaining data sensitivity constraints. The optimization of task distribution across this three-tier architecture is formulated as a multi-objective problem, minimizing both system latency and energy consumption. To solve this problem, a fractal-based multi-objective optimization algorithm is proposed to efficiently explore Pareto-optimal task allocation strategies by employing recursive space partitioning aligned with the hierarchical computing structure. Simulation experiments across varying task scales demonstrate that the proposed method achieves a 20.28% latency reduction and 3.03% energy savings compared to typical and advanced methods for large-scale task scenarios, while also exhibiting superior solution consistency and convergence. A case study on a digital twin manufacturing system validated its practical effectiveness, with CFHCC outperforming traditional cloud–edge collaborative computing by 12.02% in latency and 11.55% in energy consumption, confirming its suitability for diverse intelligent manufacturing applications.