One of the famous approaches to decision making is named as multicriteria decision making (MCDM). In order to solve the MCDM issues, a better way is provided by the fuzzy logic. Expendability, cost, maintenance, availability of software, and performance characteristics are such problems considered by the decision. The precise estimation of the pertinent data is one of the vital phases in DM systems. This paper presents a fuzzy MCDM‐based cluster head (CH) selection and hybrid routing protocol to solve the most common issues. In this research article, the generalized intuitionistic fuzzy soft set (GIFSS) approach is utilized to select the optimal CH, and hybrid shark smell optimization (SSO), and a genetic algorithm (GA) is introduced for the effective routing. Initially, the wireless sensor network (WSN) system and energy models are designed, and then the nodes are grouped into several clusters. Next, based on the GIFSS, the CH nodes are selected, and finally, an effective routing is placed based on the hybrid optimizations. The implementation is performed on the NS2 platform, and the performances are evaluated by packet delivery ratio (PDR), delay, packet loss ratio (PLR), network lifetime, bit error rate (BER), energy consumption, throughput, and jitter. The existing approaches named energy centers examining using particle swarm optimization (EC‐PSO), variable dimension‐based PSO (VD‐PSO), energy‐efficient PSO‐based CH selection (PSO‐ECHS), low‐energy adaptive clustering hierarchy‐sugeno fuzzy (LEACH‐SF), SSO, and GA are compared with the proposed strategy. According to the implemented outcomes, it displays the proposed strategy and gives improved outcomes than the others.

中文翻译：

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WSN的基于模糊多准则决策的CH选择和混合路由协议

决策的著名方法之一就是多准则决策（MCDM）。为了解决MCDM问题，模糊逻辑提供了一种更好的方法。决策考虑了可扩展性，成本，维护，软件可用性和性能特征等问题。对相关数据的精确估计是DM系统的重要阶段之一。本文提出了一种基于模糊MCDM的簇头（CH）选择和混合路由协议，以解决最常见的问题。本文采用广义直觉模糊软集（GIFSS）方法选择最优的CH，并结合鲨鱼气味优化算法（SSO）和遗传算法（GA）进行有效路由。最初，设计无线传感器网络（WSN）系统和能源模型，然后将节点分为几个集群。接下来，基于GIFSS，选择CH节点，最后基于混合优化放置有效的路由。该实现是在NS2平台上执行的，其性能由数据包传送率（PDR），延迟，数据包丢失率（PLR），网络寿命，误码率（BER），能耗，吞吐量和抖动来评估。现有的方法被称为能源中心，使用粒子群优化（EC-PSO），基于可变维的PSO（VD-PSO），基于节能PSO的CH选择（PSO-ECHS），低能耗自适应聚类层次结构-sugeno进行检查模糊（LEACH-SF），SSO和GA与提出的策略进行了比较。根据已执行的结果，它显示了拟议的策略并提供了比其他方法更好的结果。