# Multi Swarm Optimization Based Clustering with Tabu Search in Wireless Sensor Network
## 2 Related Works  
When there is an imbalance in the transferring information in the communication channel, an energy hole is created, causing the premature death of the SNs, which decreases the network's lifetime [11; 12]. This problem was resolved by [PERSON] et al. [13; 14], who proposed an approach in which the transmission range was adjusted based on the distance between CHs and their members. Also, the lifetime of the network was increased by the application of the firefly optimization algorithm. This algorithm put forth was compared with standardized algorithms under several situations. The outcome of their study showed that this algorithm showed better results concerning the life of the network. However, the author does not address the SNs' overall energy maximization while maintaining the ideal CH distance. Three objectives were put forth by [PERSON] et al. [15] to tackle the coverage problem in WSN optimization to balance between the lifetime of the network and coverage. These minimize the consumption of power and rate of coverage while maximizing its balance. Two developed hybrid multi-objective evolutionary algorithms based on decomposition, namely hybrid-MOEA/D-I and hybrid-MOEA/D-II, have been put forth; using the famous multi-objective evolutionary algorithm on decomposition (MOEA/D), hybrid-MOEA/D-I, hybrid genetic algorithm (GA), and a differential evolutionary (DE) algorithm which can efficiently optimize the coined multi-objective optimization problem. With the integration of discrete particle swarm algorithm, it was found to further improve solutions created using Hybrid-MOEA/D-I in a new Hybrid-MOEA/D-II algorithm, as MOEA is not a computationally efficient algorithm and not suitable for balancing the SN's energy and coverage. Thus to maximize the energy efficiency of the SN, the controlled algorithm should be computationally efficient. Thus we are motivated to develop a computationally efficient hybrid algorithm for getting the said solution in the stipulated time frame. PSO-UFC (PSO-based Unequal and Fault tolerant Clustering protocol) was put forth by [PERSON] and [PERSON] [16]. Imbalanced clustering and fault tolerance issues in the current Energy-Balanced Unequal Clustering (EBUC) technique were addressed in the technique proposed for the effective functioning of the network over the long run. To solve the imbalance in a clustering problem, a lack of inequality in the clustering mechanism was utilized to poise intra- and inter-cluster energy utilization among the Master CHs (MCHs). Furthermore, in the PSO-UFC protocol, there was a restoration of network connectivity through the choice of an extra CH called Surrogate CH because of an impulsive MCH failure. However, it is observed that the lifetime maximization of SNs depends on the optimized clustering strategy while reducing end-to-end delay. Thus, the motivation is to develop an optimal clustering technique with minimal packet loss and end-to-end delay to meet the essential requirement towards maximizing the SNs lifetime. Regional Energy Aware Clustering with Isolated Nodes (REAC-IN) was presented by [PERSON] et al. [17] for clustering. The choice of CH is done in REAC-In by weight and weight, which is measured through residual energy of every sensor and the mean regional energy of entire sensors in the individual cluster. Good results were shown by this technique compared to the available WSN protocols, but many issues have been neglected. To get over these restrictions, a new, improved technique was proposed. It was shown that the proposed technique could overcome the constraints of the REAC-IN routing technique through the use of clustering and TS. The available techniques were outperformed by their proposed technique. The issue of the clustering algorithm's complexity, which is a critical component in the views of maximizing the lifetime of SNs, is still unaddressed.