Salp Swarm Algorithm for Node Localization in Wireless Sensor Networks

Abstract

Nodes localization in a wireless sensor network (WSN) aims for calculating the coordinates of unknown nodes with the assist of known nodes. The performance of a WSN can be greatly affected by the localization accuracy. In this paper, a node localization scheme is proposed based on a recent bioinspired algorithm called Salp Swarm Algorithm (SSA). The proposed algorithm is compared to well-known optimization algorithms, namely, particle swarm optimization (PSO), Butterfly optimization algorithm (BOA), firefly algorithm (FA), and grey wolf optimizer (GWO) under different WSN deployments. The simulation results show that the proposed localization algorithm is better than the other algorithms in terms of mean localization error, computing time, and the number of localized nodes.

Voltage and frequency control in conventional and PV integrated power systems by a particle swarm optimized Ziegler–Nichols based PID controller

“…The number of iterations that the algorithm takes to attain the desired output is also shown for each considered system. In the proposed method, N = 40 [15], = 0.728 and c 1 and c 2 are considered as 1.494 [26]. All the systems are simulated for 10 times and the best results among them are presented in the following section.…”

An Efficient Approximation for Nakagami‐m Quantile Function Based on Generalized Opposition‐Based Quantum Salp Swarm Algorithm

“…e expression we propose meets this requirement F − 1 S (proposed) (0) � 0, and the excellent performance will be presented in Section 5. [14] and has been used in several aspects including wireless sensor networks [15], feature selection [16][17][18], parameter estimation [19], and clustering [20]. Salps usually exist in the form of a swarm called salp chain in deep oceans.…”

“…As for followers, we use the third strategy to update their quantum rotation angles. Equations (15) and (16) give the third strategy, we first compute an auxiliary quantum rotation angle…”

“…with(15), and then the quantum rotation angle δ t+1 r,d is the average value of the auxiliary quantum rotation angle⌣ δ t+1 r,d and the quantum rotation angle of the salp in front of current salp δ t+1 r− 1,d .…”

Modified Harris Hawks Optimizer for Solving Machine Scheduling Problems

“…SSA is also a nature-inspired method that simulates the behavior of Salpidae's family. It was employed in different fields, such as node localization in wireless sensor network [47], feature selection [48][49][50], prediction models [51], cloud computing [52], and image segmentation [53,54]. From these applications for HHO and SSA, it has been observed the high ability of both of them to find the optimal solution for different optimization problems.…”