Task Offloading Strategy Based on Improved Particle Swarm Algorithm in MEC

doi:10.19678/j.issn.1000-3428.0069852

Abstract

Abstract:

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.

Key words: edge computing, task offloading, parallel transmission, load balancing, Particle Swarm Optimization (PSO) algorithm

摘要：

针对多基站多任务移动边缘计算(MEC)环境中任务卸载问题, 在同时考虑任务在基站之间并行传输、任务卸载系统时延和边缘服务器负载的情况下, 提出以系统时延和负载均衡为最小化优化目标的任务卸载策略。为求解优化问题, 提出一种基于改进粒子群优化(PSO)算法的任务卸载方法IPSO, 通过采用反向学习策略对PSO算法的初始解空间进行优化, 提升算法的收敛与搜索能力。在此基础上, 利用Levy飞行策略对各粒子的速度矢量进行更新, 有效避免局部最优解, 同时引入遗传算法的精英保留策略, 得到一个能够稳定降低边缘服务器负载的任务卸载策略。将所提出的IPSO算法与混合启发式算法(GA-BPSO)、PSO算法、人工蜂鸟算法(AHA)、遗传算法(GA)和随机编码算法进行对比。实验结果表明, IPSO算法在不同的任务数、边缘服务器数下得到的时延和负载标准差都小于其他5种算法, 其中, 在任务数增长下进行实验后得到的系统时延分别比其他对比算法平均降低了3.04%、4.63%、6.79%、8.94%、12.7%, 边缘服务器之间负载标准差分别比其他对比算法平均降低了16.2%、26.4%、62.8%、71.3%、91.5%。

关键词: 边缘计算, 任务卸载, 并行传输, 负载均衡, 粒子群优化算法

WU Bo, LONG Tingyan, WAN Liang, XIA Yunni. Task Offloading Strategy Based on Improved Particle Swarm Algorithm in MEC[J]. Computer Engineering, 2026, 52(4): 327-338.

吴波, 龙廷艳, 万良, 夏云霓. MEC中基于改进粒子群优化算法的任务卸载策略[J]. 计算机工程, 2026, 52(4): 327-338.

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Figures/Tables 10

Fig.1 System scenario

Fig.2 Task forwarding process

Fig.3 Edge scene topology at w=5

Fig.4 Comparison of simulation results for different weight coefficients

Fig.5 Convergence curve of each algorithm

Fig.6 Load standard deviation under different number of tasks

Fig.7 System delay under different number of tasks

Fig.8 Edge scene topology at w= 4, 6

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