摘要： 随着移动通信技术的发展，传统智能终端设备无法满足快速增长的海量数据计算要求，移动边缘计算为物联网中移动用户提供了低延迟和灵活的计算方案。综合考虑边缘服务器上有限的计算资源以及网络中用户的动态需求，提出通过二进制粒子群优化算法分配发射功率优化传输能耗。将请求卸载与资源调度作为双重决策问题进行分析，基于粒子群优化算法提出了一种新的多目标优化算法求解该问题。仿真结果表明，二进制粒子群优化算法可以节省传输能耗，且具有良好的收敛性。所提出的新算法在响应率方面优于现有算法，在动态边缘计算网络中可以保持良好的性能。

中图分类号：TN929.5；TN301.6
文献标志码：A
DOI: 10.16157/j.issn.0258-7998.223278
中文引用格式： 彭昇，赵建保，魏敏捷. 基于5G架构超密集组网粒子群优化算法改进[J]. 电子技术应用，2023，49(1)：69-74.
英文引用格式： Peng Sheng，Zhao Jianbao，Wei Minjie. Improvement of particle swarm algorithm based on ultra-dense networking under 5G architecture[J]. Application of Electronic Technique，2023，49(1)：69-74.

Improvement of particle swarm algorithm based on ultra-dense networking under 5G architecture

Abstract： With the development of mobile communication technology, traditional intelligent terminal devices cannot meet the rapidly growing massive data computing requirements. Mobile edge computing provides low-latency and flexible computing solutions for mobile users in the Internet of Things. Considering the limited computing resources on the edge server and the dynamic needs of users in the network, this paper proposes to allocate the transmit power to optimize the transmission energy consumption through the binary particle swarm optimization algorithm. Analyzing request offloading and resource scheduling as a dual decision-making problem, a new multi-objective optimization algorithm based on particle swarm optimization algorithm is proposed to solve the problem. The simulation results show that the binary particle swarm optimization algorithm can save transmission energy and has good convergence. The proposed new algorithm outperforms existing algorithms in terms of response rate and can maintain good performance in dynamic edge computing networks.

Key words : edge computing；resource optimization；particle swarm optimization；task offloading