Recently Mobile adhoc networks (MANETs) have received the great attention of researchers as these models provide a wide range of applications. But MANET nodes are prone to various security threats. To overcome this issue, many trust management frameworks have been implemented in the literature. It has been found that the use of machine learning can predict trust values more efficiently. However, machine learning performance suffers from the hyper-parameters tuning and over-fitting issues. Therefore, in this paper, novel trust management is proposed. initially, the Adaptive neuro-fuzzy inference system (ANFIS) is used to train the trust prediction model. Thereafter, a non-dominated sorting genetic algorithm-III (NSGA-III) is used to tune the hyper-parameters of the ANFIS model. Precision, recall, and root mean squared error metrics are used to design a multi-objective fitness function. Optimized link state routing (OLSR) protocol is used for comparative analyses purpose. Three different attacks are applied on the designed network i.e., link spoofing, jellyfish, and gray hole attacks to obtain the dataset. Comparative analysis reveals that the proposed trust evaluation model outperforms the competitive trust evaluation models in terms of various performance metrics such as routing overheads, average end to end latency, packet delivery ratio, and throughput. Thus, the proposed protocol is more secure against various security threats.

最近，移动自组织网络（MANET）受到了研究者的极大关注，因为这些模型提供了广泛的应用。但是MANET节点容易受到各种安全威胁。为了克服这个问题，文献中已经实现了许多信任管理框架。已经发现，使用机器学习可以更有效地预测信任值。但是，机器学习性能会受到超参数调整和过度拟合问题的困扰。因此，本文提出了一种新颖的信任管理方法。最初，自适应神经模糊推理系统（ANFIS）用于训练信任预测模型。此后，使用非支配排序遗传算法-III（NSGA-III）来调整ANFIS模型的超参数。精度，召回率，和均方根误差度量用于设计多目标适应度函数。优化的链接状态路由（OLSR）协议用于比较分析目的。在设计的网络上应用了三种不同的攻击，即链路欺骗，水母和灰洞攻击，以获取数据集。比较分析表明，在各种性能指标（例如路由开销，平均端到端等待时间，数据包传输率和吞吐量）方面，所提出的信任评估模型优于竞争性信任评估模型。因此，提出的协议针对各种安全威胁更加安全。水母和灰洞攻击来获取数据集。比较分析表明，在各种性能指标（例如路由开销，平均端到端等待时间，数据包传输率和吞吐量）方面，所提出的信任评估模型优于竞争性信任评估模型。因此，提出的协议针对各种安全威胁更加安全。水母和灰洞攻击来获取数据集。比较分析表明，在各种性能指标（例如路由开销，平均端到端等待时间，数据包传输率和吞吐量）方面，所提出的信任评估模型优于竞争性信任评估模型。因此，提出的协议针对各种安全威胁更加安全。