Energy-efficient clustering and routing are two well-known optimization problems, mainly employed to achieve energy efficiency and maximum network lifetime in wireless sensor networks (WSNs). The clustering and routing processes can be considered as an NP-hard problem, and metaheuristic algorithms can be applied to resolve it. In this paper, a dynamic clustering and process protocol based on multi-objective particle swarm optimization with Levy distribution (MOPSO-L) algorithm. Since the parameters in WSN are related to one another, multi-objective parameters should be included in the process of cluster head selection and routing. The proposed MOPSO-L technique is presented for organizing the clusters and CH chosen by merging consolidated and shared models. The MOPSO-L algorithm incorporates the benefits of PSO algorithm along with the merits of Levy distribution to escape from trapping into local optima. The presented model undergoes comparison with existing techniques under three different scenarios based on the location of the BS with respect to average energy consumption, number of data transmission, and network lifetime. The experimental outcome reveals that the proposed model attains extended network lifetime as well as efficient energy over its comparatives.

中文翻译：

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使用多目标粒子群优化和 Levy 分布的无线传感器网络动态聚类和路由

节能聚类和路由是两个众所周知的优化问题，主要用于在无线传感器网络 (WSN) 中实现能源效率和最长网络寿命。聚类和路由过程可以被认为是一个 NP-hard 问题，并且可以应用元启发式算法来解决它。在本文中，一种基于多目标粒子群优化与 Levy 分布（MOPSO-L）算法的动态聚类和处理协议。由于 WSN 中的参数是相互关联的，因此在簇头选择和路由过程中应包含多目标参数。提出的 MOPSO-L 技术用于组织通过合并合并和共享模型选择的集群和 CH。MOPSO-L 算法结合了 PSO 算法的优点以及 Levy 分布的优点，可以避免陷入局部最优。所提出的模型在三种不同场景下根据 BS 的位置在平均能耗、数据传输数量和网络寿命方面与现有技术进行了比较。实验结果表明，所提出的模型与其比较相比，获得了延长的网络寿命以及有效的能量。