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

Abstract

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.

A Systematic Study of Clustering Techniques for Energy Efficiency in Wireless Sensor Networks

“…Metaheuristic techniques may be broken down into two categories: those that rely on population data (random search) and those that rely on single-solution local searches [65]. The capacity of the metaheuristic algorithm to balance exploration and exploitation is crucial for achieving an optimal solution since this prevents the algorithm from becoming stuck at a local optimum or slowly converges [66]. A nonhybrid metaheuristic approach to optimization problems is an algorithm that doesn't use the algorithmic parts of other methods.…”

“…A modified gravity search technique (GSA) is presented in [66]. A middle ground between exploration and exploitation must be found, the GSA has been changed to include a tournament selection method and a changing mass value over time.…”

A Survey on Cluster Head Selection and Cluster Formation Methods in Wireless Sensor Networks

“…The word metaheuristic is split into two, meta and heuristic, where meta means a high-level methodology and heuristic refers to a technique of solving problems by finding new strategies [80]. To achieve an optimal solution, it is very important for the metaheuristic algorithm to balance its exploration and exploitation capability so that the algorithm does not fall into local optimum easily or has a slow convergence rate [81]. A nonhybrid metaheuristic method refers to an algorithm that has no inclusions of other techniques' algorithmic components to solve optimization problems.…”

“…Mood and Javidi on the other hand proposed a modified gravitational search algorithm (GSA) in WSNs [81]. Since it is very important to have a balance between exploitation and exploration, GSA is modified with varying mass value over time and the inclusion of a tournament selection method.…”