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The rapid development of mobile communication devices has brought challenges to wireless networks, where data packets are able to organize and maintain local area networks more freely without the constraints of wired devices. Scholars have developed diverse network protocols on how to ensure data transmission while maintaining its self-organizational nature. However, it is difficult for traditional network protocols to meet the needs of increasingly complex networks. In order to solve the problem that the better node set may not be selected when selecting the node set responsible for forwarding in the traditional OLSR protocol, a multi-objective optimized OLSR algorithm is proposed in this paper, which incorporating a new MPR mechanism and an improved NSGA-II algorithm. In the process of route discovery, the intermediate nodes responsible for forwarding packets are determined by the new MPR mechanism, and then the main parameters in the OLSR protocol are provided by the multi-objective optimization algorithm. Matlab was used to build a self-organizing network in this study. In addition, the conventional OLSR protocol, NSGA-II algorithm and multi-objective simulated annealing algorithm are selected to compare with the proposed algorithm. Simulation results show that the proposed algorithm can effectively reduce packet loss and end-to-end delay while obtaining better results in HV and Spacing, two multi-objective optimization result evaluation metrics. [ABSTRACT FROM AUTHOR]

Background: VCI is a severe public health problem facing the world today. In addition to pharmacological treatment, non-invasive neuromodulation techniques have also been effective. At this stage, non-invasive neuromodulation techniques combined with pharmacological treatment are the mainstay of clinical treatment, and clinical trials are continuing to be conducted, which is becoming the direction of treatment for VCI. Therefore, we outline this systematic review and network meta-analysis protocol to evaluate and rank clinical data in future studies which can develop optimal protocols for the clinical treatment of VCI with non-invasive neuromodulation techniques in combination with drugs. Methods: The network meta-analysis will search eight databases, including PubMed, Embase, Cochrane Library, Web of Science, China Knowledge Infrastructure Library (CNKI), China Biology Medicine disc (CBM)), Wanfang Data Knowledge Service Platform and Vipshop Journal Service Platform (VIP), for a period of from the establishment of the library to January 30 2022. The quality of the studies will be evaluated using the Cochrane Review's Handbook 5.1 and the PEDro scale to assess the evidence and quality of the included randomised controlled trials. Risk of bias assessment and heterogeneity tests will be performed using the Review Manager 5.4 program, and Bayesian network meta-analysis will be performed using the Stata 16.0 and WinBUGS 1.4.3 program. Results: The results of the network meta-analysis will be published in a peer-reviewed journal. Conclusions: Our study is expected to provide high quality evidence-based medical evidence for the treatment of VCI by clinicians. Trial registration: PROSPERO:CRD42022308580. [ABSTRACT FROM AUTHOR]