# Multi Swarm Optimization Based Clustering with Tabu Search in Wireless Sensor Network
###### Abstract  
Wireless Sensor Networks (WSNs) can be defined as a cluster of sensors with a restricted power supply deployed in a specific area to gather environmental data. One of the most challenging areas of research is to design energy-efficient data gathering algorithms in large-scale WSNs, as each sensor node, in general, has limited energy resources. Literature review shows that with regards to energy saving, clustering-based techniques for data gathering are quite effective. Moreover, cluster head (CH) optimization is a non-deterministic polynomial (NP) hard problem. Both the lifespan of the network and its energy efficiency are improved by choosing the optimal path in routing. The technique put forth in this paper is based on multi swarm optimization (MSO) (i.e., multi-PSO) together with Tabu search (TS) techniques. Efficient CHs are chosen by the proposed system, which increases the optimization of routing and life of the network. The obtained results show that the MSO-Tabu approach has a 14%, 5%, 11%, and 4% higher number of clusters and a 20%, 6%, 14%, and 6% lesser average packet loss rate as compared to a genetic algorithm (GA), differential evolution (DE), Tabu, and MSO based clustering, respectively. Moreover, the MSO-Tabu approach has 136%, 36%, 136%, and 38% higher lifetime computation, and 22%, 16%, 51%, and 12% higher average dissipated energy. Thus, the study's outcome shows that the proposed MSO-Tabu is efficient, as it enhances the number of clusters formed, average energy dissipated, lifetime computation, and there is a decrease in mean packet loss and end-to-end delay. cluster head (CH); energy consumption; metaheuristics; particle swarm optimization (PSO); wireless energy transfer 20  
WSNs; (4) In WSN, there is design constrain as many desirable components are prohibited by the integration because of energy, storage, and other difficulties and so a big challenge in energy conservation. Researchers have resolved this issue more recently by forming clusters from nodes, thus improving its lifespan [3; 4]. The network's lifetime is increased by monitoring regions classified into several regions called clusters. Many sensor nodes are found in each cluster. Only one sensor operates as the cluster head (CH), while the remaining work as cluster members. The information is collected from respective sensors in the specified region. During the selection of CH in every region, the node with the increased energy residue compared to other nodes is chosen as CH [5]. To enhance the network's lifetime, the most crucial part is the selection of the CH. The various steps for this can be grouped as (i) node with maximum power residue, (ii) rotating CH at specific interludes, (iii) CH selection based on the rotation of communication among the sink and CH, (iv) algorithms for optimized CH selection. In cluster WSNs, implementation of routing protocols is done to provide guidance to the choice of CHs and locate the best passage that can save the node's energy. When the network has multiple clusters, it is called clustering routing; every cluster has a tree structure that consists of several CH and cluster members. The information gathered by cluster members is passed on to CH to be compiled through multi-hop CHs or directly passed on to the BS. The outcome of the clustering routing algorithm in each round of CH selection benefits energy load balancing of the network node; the data that is collected by the member nodes of the cluster is forwarded by CH after a merger to mitigate the energy consumed by the network to transmit data and avoid every cluster member node that forwards data individually [6; 7]. For WSNs, clustering routing algorithms are widely used; the classic clustering routing algorithms such as Low Energy Adaptive Clustering Hierarchy (LEACH), Hybrid Energy Efficient Distributed clustering protocol (HEED), and other agreements. The issue of energy-balanced selection of CH is NP-hard. Many detailed reviews on these are available in the literature [8; 9].