###### Abstract
[10] proposed a minimum area localization algorithm for HWSNs combined with the Harris Hawk optimization algorithm, but the algorithm lacks a comparison with the use of other metaheuristic algorithms, and it is difficult to highlight the advantages of using HHO. No single meta-heuristic algorithm is suitable for the engineering applications used, so it is necessary to find a suitable metaheuristic algorithm in combination with specific application scenarios. Meta-heuristic algorithms are mostly developed around physical phenomena, biological evolution, and group intelligence [11; 12]. Among the many meta-heuristic algorithms, the most commonly used is the swarm intelligence optimization algorithm, such as the Harris Hawk optimization algorithm (HHO) [13], grey wolf optimizer (GWO) [14], butterfly optimization algorithm (BOA) [15], and so on. The sparrow search algorithm (SSA) [16] is a typical swarm intelligence optimization algorithm, which was proposed by [PERSON] et al. in 2020. It has been widely used in many engineering fields, including UAV path planning [17], price prediction [18], and wind energy prediction [19]. In fact, the sparrow search algorithms suffer from the same problems as other meta-heuristic algorithms, such as low optimization-seeking accuracy and slow convergence. Thus, SSA needs to be improved. [PERSON] et al. [20] used a chaotic mapping strategy to initialize the population and introduced adaptive weighting strategy and t-distribution variation strategy to balance the local exploration ability and global utilization ability of the algorithm. [PERSON] et al. [21] introduced centroid opposition learning, learning coefficients, and mutation operators in the original SSA to prevent SSA from falling into the local optima, and applied the improved algorithm to the control of distributed maximum power point tracking. [PERSON] et al. [22] proposed a hybrid sparrow search algorithm based on constructing similarity, which overcomes the problem of the algorithm falling into a local optimum by introducing an improved chaotic mapping circle and t-distribution variation. In summary, this paper compares the performance of 15 common meta-heuristic algorithms in node localization, and the comparison results are shown in Section 4. Then, SSA with better comprehensive performance is selected as the algorithm for the coordinate calculation stage, and SSA is improved with regard to its problems. Finally, the improved sparrow search algorithm (ISSA) is applied to the node localization of HWSNs. The main contributions of this paper are as follows:  
* An improved sparrow search algorithm that incorporates the golden sine strategy, particle swarm optimal idea, and Gaussian perturbation is proposed. It shows a better performance in finding the optimal than the sparrow search algorithm and other comparative algorithms;
* ISSA is applied to the problem of solving the coordinates of unknown nodes in HWSNs. It achieves better localization accuracy compared with the localization algorithm using the remaining 15 meta-heuristic algorithms and LS. The rest of this paper is organized as follows. In Section 2, the original SSA is introduced, and ISSA is presented to address the problems of slow convergence and low accuracy of SSA. In Section 3, simulation results are described and discussed. In Section 4, the application of ISSA in the node localization of HWSNs is introduced. Finally, Section 5 gives the summary of this paper and the direction of future work.