Range-Free Localization Scheme Based on a Reconstructed Marine Predators Algorithm With Adaptive Enhancement for Wireless Sensor Networks

Wireless sensor networks (WSN) are widely utilized in all walks of industries, and node localization is still essential as one of the fundamental functionalities. Moreover, the marine predators algorithm (MPA), as one of the swarm intelligence optimization algorithms, has proven to possess strong search capability and high convergence speed. Therefore, for the poor optimization ability of the least squares method in the DV-Hop method, a reconstructed marine predator algorithm with adaptive enhancement (RMPA-AE) for WSN node localization is proposed in this paper. Firstly, a population diversity expression based on Euclidean distance is proposed to reconstruct the phases of the algorithm. Then, an adaptive enhancement strategy is proposed to improve the local exploitation ability of the algorithm with respect to its tendency to fall into local optimality. Subsequently, a global perturbation strategy based on the iteration number is proposed for the re-generation of the population at FADs to achieve a wide range of individual jumps. Finally, the experimental results for 26 benchmark functions demonstrate the search capability of the proposed RMPA-AE algorithm, and the superiority of the proposed algorithm compared to the state-of-the-art algorithms. The feasibility of the proposed algorithm is also demonstrated in the node localization simulation experiments.