Self-Adaptive Evolutionary Multitasking Algorithm for Mobile Edge Computing in Internet of Things

With the advancement of Internet of Things (IoT) technology, IoT smart devices (ISDs) are often frequently constrained by their battery capability and computing ability. In mobile edge computing (MEC) scenarios, ISDs increases computing capability while reducing energy consumption by offloading computation-intensive and latency-critical processes to edge servers. But the process of computing offloading (CO) leads to extra transmission time, which is unacceptable for IoT applications. Most current studies regard the CO as a multi-objective optimization problem involving application completion time and energy consumption. However, with the increasing number of ISDs and the complexity of ISDs-based applications, it becomes extremely challenging to acquire the best solution through multi-objective optimization. Evolutionary multitasking optimization (EMTO), as a novel search paradigm in evolutionary computation, utilizes inter-task correlation and facilitates the solution of multiple tasks simultaneously. Therefore, we model the CO of ISDs as a multitasking multi-objective CO problem (MMCOP) by constructing an auxiliary task similar to the target task for the first time. In addition, we put forward a multitasking multi-objective optimization algorithm based on reinforcement learning (RLMTO-CO) to solve MMCOP. Each task has a knowledge transfer probability value adaptively adjusted by reinforcement learning, and the generation of auxiliary tasks is updated adaptively. The performance of RLMTO-CO has been validated in different test cases close to true IoT environments, and the experimental results show the superior competitiveness.