Cooperative Evolutionary Computation for Multi-Rat Edge Computing

Multi-radio access technology (multi-RAT) enabled mobile edge computing (MEC) has emerged as a promising paradigm for supporting heterogeneous applications. However, efficiently managing resources for both ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) services in large-scale networks remains challenging. In this paper, we investigate a joint optimization problem involving user association and bandwidth allocation in multi-RAT-enabled MEC systems. We propose a novel cooperative evolutionary framework operated based on the interplay between inner and outer agents to efficiently optimize large-scale networks. Extensive simulation results demonstrate that the proposed approach significantly outperforms the conventional single-RAT MEC system and several representative evolutionary computation algorithms.