A modified grasshopper optimization algorithm based on levy flight for cluster head selection in wireless sensor networks

A wireless sensor network (WSN) is made up of numerous wireless sensors that may be used for a variety of purposes, including security surveillance, terror threat detection, health monitoring, and environmental monitoring. In these applications, thousands of wireless sensors are deployed in remote environments to operate autonomously. The wireless sensor nodes are largely confined by limited energy supply, memory, and bandwidth. Major issues in designing WSNs are energy consumption and maximizing the network lifetime. Low energy adaptive clustering hierarchy (LEACH) is a reliable routing protocol that utilizes the cluster head rotation strategy to uniformly allocate the energy burden among all the available nodes. LEACH maintains the steadiness of the energy consumed by the nodes. However, LEACH protocol does not guarantee the uniform allotment of the cluster heads (CHs), and eventually reduces the network lifetime. A clustering protocol offers a potential solution that guarantees energy saving of nodes and increases the lifetime of the network by organizing nodes into clusters to reduce the transmission distance between sensor nodes and the base station (BS). The traditional grasshopper optimization algorithm (GOA) has a set of shortcomings such as the ease with which it can fall into local optimum and the slow convergence speed. To address these drawbacks, a modified grasshopper optimization algorithm (MGOA) was proposed based on an energy efficient routing protocol in LEACH. It is called as modified grasshopper optimization algorithm, low energy adaptive clustering hierarchy (MGOA-LEACH). It has been proposed to minimize the energy consumption and maximize the network lifetime in WSNs. The levy flight (LF) strategy was used to increase the randomness of the search agent's movement, allowing GOA to have a greater global exploration capability. The evaluation results show that the suggested algorithm provides lower energy consumption and better life time compared to competitive clustering algorithms like LEACH, genetic algorithm (GA), particle swarm optimization (PSO), whale optimization algorithm (WOA), GOA.