Computer Engineering and Applications ›› 2018, Vol. 54 ›› Issue (14): 203-210.DOI: 10.3778/j.issn.1002-8331.1707-0066

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Design optimization of near space communication network based on improved-NSGA2

TANG Shuzhu, YOU Peng, YAN Dawei, YONG Shaowei   

  1. Institute of Electronic Science and Engineering, National University of Defense Technology, Changsha 410005, China
  • Online:2018-07-15 Published:2018-08-06


唐树祝,游  鹏,闫大伟,雍少为   

  1. 国防科学技术大学 电子科学与工程学院,长沙 410005

Abstract: In the deployment of near space communication network for specific area, factors such as resource allocate, coverage rate and load power should be taken into account. Considering the deficiency of exiting methods only optimizing single objective, and using the Non-dominated Sorting Genetic Algorithm 2(NSGA2) in solving practice problems, a multi-objective optimization design method based on improved-NSGA2 is presented. Analyzed the downlink performance, combined the target area’s demand distribution, the model is established with objectives of maximizing match degree and coverage rate, and minimizing power cost. The dynamic opposition-based learning mechanism and the differential local mutation operator are introduced into NSGA2. Tests prove that solutions obtained from improved-NSGA2 perform better both in convergence and distribution. The effectiveness of the method is verified by simulation, which provides a reference for the network’s actual deployment.

Key words: near space communication network, multi-objective optimization, Non-dominated Sorting Genetic Algorithm 2(NSGA2)

摘要: 面向特定区域部署的临近空间通信网络需要兼顾考虑资源分配、覆盖率及载荷功率等多个因素。考虑到现有方法只采用单目标优化,以及非支配排序遗传算法(NSGA2)在求解实际问题时的缺陷等问题,提出一种基于改进NSGA2的临近空间通信网络多目标优化设计方法。分析下行链路性能,结合目标区域的需求分布,以最大化匹配度和覆盖率及最小化功耗代价为目标建立多目标优化模型。将动态反向学习机制和差分局部变异算子引入NSGA2,测试证明改进的NSGA2在解收敛性和分布性上表现更好。仿真验证了设计方法的有效性,为网络的实际部署提供了参考。

关键词: 临近空间通信网络, 多目标优化, 非支配排序遗传算法