Joint Optimization in MEC Incorporating MD Preference: A Hybrid GA and AFSA Scheme

Mobile edge computing (MEC) is an efficient method to tackle computationally intensive tasks for mobile devices (MDs). However, current studies about MEC do not consider that different MDs have different preferences for delay and energy consumption. Thus, we propose a MD preference-based MEC computing model addressing three optimization goals: delay preference, energy preference, and their balance, tailored to diverse MD preference. This optimization problem is formulated as a mixed integer non-linear programming (MINLP) task with four optimization variables: offloading decisions, channel allocation, power allocation, and resource allocation. Additionally, we propose a novel genetic artificial fish swarm cooperative optimization algorithm (GAFSCOA) to solve this problem, which integrates genetic algorithm (GA) and artificial fish swarm algorithm (AFSA), respectively. Numerical results show our proposed model can achieve different optimization goals according to MDs’ preferences. Compared with GA and AFSA, our proposed GAFSCOA demonstrates 30.71% faster convergence speed and 46.09% better optimization results. Furthermore, in comparison with other baseline algorithms, our algorithm yields superior optimization results.