A fault-tolerant task offloading framework via large-scale multi-objective evolutionary optimization and game-based decision mechanism

Large-scale multi-access edge computing (MEC) optimization is challenging due to high-dimensional decision spaces, conflicting objectives, nonstationary conditions, and failure-prone infrastructure. This paper presents an adaptive Mahalanobis distance-based large-scale multi-objective evolutionary algorithm with knowledge transfer and a two-layer encoding (ALC-LSMOEA-KT). The task-offloading model optimizes latency, energy, load balance, and failure risk under communication and computation constraints. A two-layer sparse encoding separates variable activation from value search, and a phase-aware evolution with Mahalanobis-guided covariance adaptation exploits inter-variable correlations while preserving diversity. A Stackelberg-based fault-tolerant migration module reassigns disrupted tasks to sustain robustness. Experiments on scalable multi-objective optimization Problems(SMOP)/large-scale multi-objective optimization problem(LSMOP) benchmarks and a realistic MEC simulator with dynamic arrivals, bandwidth variation, and injected failures show consistent gains in inverted generational distance (IGD), solution diversity, and robustness. The results indicate a scalable and reliable approach to MEC optimization under high dimensionality and uncertainty.