A modified gravitational search algorithm and its application in lifetime maximization of wireless sensor networks

Recently, academic communities and industrial sectors have been affected by significant advancements in wireless sensor networks (WSNs). Employing clustering methods is the dominant method to maximize the WSN's lifetime, which is considered to be a major issue. Metaheuristic algorithms have attracted wide attention in the research area of clustering. In this paper, first a novel nature-inspired optimization algorithm based on the gravitational search algorithm (GSA) is defined. To control the exploitation and exploration capabilities of this algorithm, along with calculating the masses value, the tournament selection method is employed. Tournament size, the parameter of this method, is computed automatically using a function during the computational process of the algorithm. The abilities of the algorithm are balanced using this problem-independent parameter. Therefore, the performance of the proposed algorithm is improved in this paper. Moreover, a modified GSA is applied to an energy-efficient clustering protocol for WSNs to minimize the objective function defining the compact clusters that have cluster heads with high energy. The proposed search algorithm is evaluated in terms of some standard test functions. The results suggest that this method has better performance than other state-of-the-art optimization algorithms. In addition, simulation results indicate that the proposed method for the clustering problem in WSNs has better performance on network lifetime and delivery data packets in BS than other popular clustering methods.