A Robust and Improved sparrow search algorithm for optimization in wireless sensor network

The emergence of swarm intelligence approaches has resulted in the development of a workable theoretical computational approach to the simulation, modeling, and optimization of complex systems. This research proposes using a Modified sparrow search algorithm (MSSA) to optimize the coverage WSN. There are three areas of the algorithm that have been improved. We first use the LHS approach to produce the initial population. To further improve the method's convergence efficiency, we offer new optimization equations based on an adaptively adjusted sine cosine algorithm and the Lévy flight strategy. Finally, a unique mutation disturbance mechanism is implemented at the conclusion of every iteration to maximize the individuals with low fitness in the natives. Experimental findings from 13 benchmark functions reveal that the proposed improved method has benefits in convergence pace, and strength, as shown by the stability of its average value and the shortness of the time it takes to reach the optimal solution. In this study, we provide a state-of-the-art optimization framework for the WSN coverage issue using swarm intelligence algorithms and then analyze the efficacy of nine methods.