基于协作智能与子梯度优化算法的电力业务差异化QoS路由策略*

徐彬泰; 周洁; 于秋生; 马超; 马良

徐彬泰,周洁,于秋生,马超,马良.基于协作智能与子梯度优化算法的电力业务差异化QoS路由策略*[J].电测与仪表,2020,57(10):50-57.
XU BinTai,ZHOU Jie,YU Qiu-sheng,MA Chao,MA Liang.Differentiated QoS Routing Strategy for Electrical Services based on Cooperative Intelligence and Subgradient Optimization Algorithm[J].Electrical Measurement & Instrumentation,2020,57(10):50-57.

Differentiated QoS Routing Strategy for Electrical Services based on Cooperative Intelligence and Subgradient Optimization Algorithm

DOI：DOI: 10.19753/j.issn1001-1390.2020.10.008

中文关键词: 群智能优化子梯度优化泛在电力物联网动态路由策略

英文关键词:swarm intelligence optimization subgradient optimization ubiquitous electrical Internet of things dynamic routing strategy

基金项目:国家自然科学基金项目（ 61701197）

作者中文名	作者英文名	单位
徐彬泰	XU BinTai	国网山东省电力公司信息通信公司
周洁	ZHOU Jie	国网山东省电力公司信息通信公司
于秋生	YU Qiu-sheng	国网山东省电力公司信息通信公司
马超	MA Chao	国网山东省电力公司信息通信公司
马良	MA Liang	国网山东省电力公司信息通信公司

摘要点击次数: 2199

中文摘要:

为不同电力业务提供端到端的、确定性的带宽、时延、丢包率、时延抖动等网络QoS,是未来泛在电力物联网通信网络支撑的关键任务之一。本文提出一种基于协作智能与子梯度优化算法的差异化QoS路由策略,解决电力业务多约束条件下路由选择收敛速度慢、易陷入次优解、解的可行性检验缺失、最优性难以证明等问题；具体而言,利用子梯度方法能够动态调整QoS多约束条件惩罚因子从而在迭代过程执行可行性检验,并在获得最终解时评估其最优性；利用基本蚁群算法改进后的协作机制,提高不同质量路径的区分度,达到快速收敛、避免进入局部次优解的目的。仿真结果表明本文提出的算法较已有方法能够更快地收敛至最优值,且提供了验证解的可行性与最优性手段。#$NL关键词：群智能优化；子梯度优化；泛在电力物联网；动态路由策略

英文摘要:

To provide end-to-end, deterministic bandwidth, delay, packet loss, delay jitter and other network QoS guarantees for diverse electrical services is one of the critical tasks in communication network underlaying ubiquitous electrical Internet of things. In this paper, a differentiated QoS routing strategy based on cooperative intelligence and subgradient optimization algorithm is proposed to address the issues of route selection under multiple constraints, such as slow convergence, easy to fall into sub-optimal solution, lack of feasibility test of solution and difficult to prove optimality. Specifically, the sub-gradient method can dynamically adjust the weighting coefficients associated with multiple QoS constraints, so as to perform feasibility check of solutions during the iteration. In addition, we put forward an improved coordination mechanism for basic ant colony algorithm, which can be used to improve the discrimination of different links and thus achieve fast convergence and avoidance of entering the local sub-optimal solution. The simulation results show that our proposed algorithm can converge to the optimal routing path faster than the existed heuristic algorithms. Besides, the proposed algorithm provides the verification of solution feasibility and optimality.#$NLKeywords: swarm intelligence optimization; subgradient optimization; ubiquitous electrical Internet of things; dynamic routing strategy

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