The selection of good rendezvous points (RPs) is a significant role for M2obile Sink (MS) in the wireless sensor network (WSN). For the mobile sink, the selection of RP is one of the major problems in WSN. The rendezvous points are only selected based on the local information, so the possibility of selecting an optimal sensor node as RP will be extremely low. The next problem is to find the mobile sink path which visits all the RPs. The above problem is comprehended by utilizing an optimization algorithm. In this paper, Adaptive Neuro‐Fuzzy Inference System‐Particle swarm optimization based clustering approach and hybrid Moth‐flame cuttlefish optimization (MFO‐CFO) algorithm for efficient routing in WSN is proposed. Initially, the number of clusters is formed due to the Fuzzy C‐means (FCM) based Ant lion optimization (ALO) clustering approach. An Adaptive Neuro‐Fuzzy Inference System (ANFIS) based Particle swarm optimization (PSO) technique is presented for best cluster head (CH) selection. Here, residual energy (RE), node degree, and histories are considered as an input parameter to find the optimal cluster head. At last, we use hybrid MFO‐CFO techniques to find the minimum RPs which decreases energy consumption. The performance metrics of end to end (E2E) delay, energy consumption, channel load, throughput, bit error rate (BER), Network Life Time (NLT), packet delivery ratio (PDR), latency, jitter, and packet loss are computed using several nodes. The performance is compared with some existing algorithms such as Genetic Approach (GA), Fuzzy, PSO (Particle Swarm Optimization), Multiple Access Data Gathering using Mobile Sink for Path Constrained Environment (MADGAMSPCE).

中文翻译：

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自适应神经模糊推理系统基于粒子群优化的聚类方法和混合飞蛾乌贼优化算法，用于无线传感器网络中的高效路由

对于无线传感器网络（WSN）中的M2obile接收器（MS）而言，良好的集合点（RP）的选择至关重要。对于移动接收器，RP的选择是WSN中的主要问题之一。仅根据本地信息选择集合点，因此选择最佳传感器节点作为RP的可能性极低。下一个问题是找到访问所有RP的移动接收器路径。通过利用优化算法来理解上述问题。本文提出了一种基于自适应神经模糊推理系统-粒子群优化的聚类方法和混合飞蛾墨鱼优化（MFO-CFO）算法在无线传感器网络中进行有效路由。最初，由于基于模糊C均值（FCM）的蚁群优化（ALO）聚类方法而形成了多个聚类。提出了一种基于自适应神经模糊推理系统（ANFIS）的粒子群优化（PSO）技术，以选择最佳的簇头（CH）。在此，将剩余能量（RE），节点度数和历史记录视为找到最佳簇头的输入参数。最后，我们使用混合MFO-CFO技术来找到最小的RP，从而降低能耗。计算端到端（E2E）延迟，能耗，信道负载，吞吐量，误码率（BER），网络生存时间（NLT），数据包传输率（PDR），延迟，抖动和数据包丢失的性能指标使用几个节点。该性能与一些现有算法进行了比较，例如遗传方法（GA），模糊算法，PSO（粒子群优化），使用针对路径受限环境的移动接收器的多路访问数据收集（MADGAMSPCE）。