Task Offloading Strategy Based on Improved Particle Swarm Algorithm in MEC

For the task unloading problem in the multibase station multitask Mobile Edge Computing (MEC) environment when considering the parallel transmission between base stations, task unloading delay, and edge server load, a task unloading strategy with system delay and load balancing is proposed. To solve optimization problems, a task offloading method, called IPSO, based on improved Particle Swarm Optimization (PSO) algorithm is proposed. This algorithm optimizes the initial solution space of the PSO algorithm, uses the flight strategy of Levy to update the speed vector of each particle, effectively avoids the local optimal solution, and introduces the elite retention strategy of genetic algorithm to obtain a task unloading policy that can stably reduce the load of edge server. The IPSO algorithm is compared with Genetic Algorithm-Binary Particle Swarm Optimization (GA-BPSO), PSO, Artificial Hummingbird Algorithm (AHA), Genetic Algorithm (GA), and random coding algorithm. The experimental results show that the time delay and load standard deviation of IPSO algorithm under different task numbers and edge servers are less than the other five algorithms. Additionally, the system delay obtained after the task number increase is 3.04%, 4.63%, 6.79%, 8.94% and 12.7% lower than that of other algorithms, respectively. Moreover, the load standard deviation is 16.2%, 26.4%, 62.8%, 71.3% and 91.5% lower than the other algorithms, respectively.