中圖分類號：TN929.5；TN301.6
文獻(xiàn)標(biāo)志碼：A
DOI: 10.16157/j.issn.0258-7998.223278
中文引用格式： 彭昇，趙建保，魏敏捷. 基于5G架構(gòu)超密集組網(wǎng)粒子群優(yōu)化算法改進(jìn)[J]. 電子技術(shù)應(yīng)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