Multifactorial Memetic Algorithm with Adaptive Auxiliary Tasks for Service Migration Optimization in Mobile Edge Computing

In high-speed mobile networks, mobile edge computing is tasked with service migration optimization, i.e., assigning mobile users to the right servers to minimize the response time. Service migration optimization is a complex problem posing significant challenges to conventional optimization methods. To tackle this problem , we develop a multifactorial memetic algorithm with adaptive auxiliary tasks or MFMA-AAT for short. MFMA-AAT solves the target service migration optimization problem and an adaptively selected auxiliary task simultaneously, where the auxiliary task is a simplified version of the target problem to guide the search towards promising regions faster via knowledge transfer. Multiple auxiliary tasks are pre-constructed based on the distribution of the mobile users and the one with best improvement at each generation is selected for knowledge transfer. A community detection based memetic operator is also introduced to accelerate the local convergence of the proposed algorithm. Experimental results on test problems demonstrate that MFMA-AAT is more efficient than traditional service migration approaches and other state-of-the-art multifactorial evolutionary algorithms.