In wireless sensor networks (WSN), computational challenges exist in determining a global energy optimized communication routing in large spaced network. WSN challenges can be handled by applying heuristic bio-inspired computational intelligence optimization methods. In WSN, Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm is most frequently used hierarchical routing algorithm in spite of certain limitations. The proposed work is addressed in this direction, to improve issues of LEACH, such as identification of reliable CH, selection of energy efficient inter and intra route communication using relay nodes, so as to extend the network lifespan. The proposed work applies Particle Swarm Optimization (PSO) and Wolf Search optimization methods to improve the performance of LEACH algorithm. PSO is castoff for cluster formation and Wolf search for identification of two relay nodes: intra and inter relay node. The Spyder-py3 tool is used to simulate the proposed algorithm: LEACH PSO Wolf search based Optimization (LEACH-PWO). The proposed work compared with original LEACH, power-efficient gathering in sensor information systems, Ant Cuckoo optimized using energy-efficient data aggregation, and Genetic Algorithm data Aggregation LEACH protocols indicate prolonged lifetime of network and increased throughput.

在无线传感器网络（WSN）中，在确定大空间网络中的全局能源优化通信路由时存在计算难题。通过应用启发式生物启发式计算智能优化方法，可以应对WSN挑战。在WSN中，尽管有某些限制，但低能耗自适应聚类层次结构（LEACH）算法是最常用的层次路由算法。朝着这个方向解决了拟议的工作，以改善LEACH问题，例如确定可靠的CH，使用中继节点选择节能的内部和内部路由通信，以延长网络寿命。拟议的工作应用粒子群优化（PSO）和Wolf搜索优化方法来提高LEACH算法的性能。PSO被淘汰以进行集群形成，而Wolf搜索则用于识别两个中继节点：内部中继节点和内部中继节点。Spyder-py3工具用于模拟所提出的算法：基于LEACH PSO Wolf搜索的优化（LEACH-PWO）。与原始LEACH，传感器信息系统中的高能效收集，使用节能数据聚合进行优化的Ant Cuckoo以及遗传算法数据聚合LEACH协议进行了比较，所提出的工作表明网络的寿命延长且吞吐量提高。