EHPSO: An Enhanced Hybrid Particle Swarm Optimization Algorithm for Internet of Things

Authors

  • Dashe Li Shandong Business and Technology University
  • Dapeng Cheng Shandong Business and Technology University
  • Jihong Qin Binzhou Medical University
  • Shue Liu
  • Pingping Liu Yantai North Tea Promotion Center

DOI:

https://doi.org/10.3991/ijoe.v14i06.8305

Abstract


Internet of Things (IOT) has found broad applications and has drawn more and more attention from researchers. At the same time, IOT also presents many challenges, one of which is node localization, i.e. how to determine the geographical position of each sensor node. Algorithms have been proposed to solve the problem. A popular algorithm is Particle Swarm Optimization (PSO) because it is simple to implement and needs relatively less computation. However, PSO is easily trapped into local optima and gives premature results. In order to improve the PSO algorithm, this paper proposes the EHPSO algorithm based on Novel Particle Swarm Optimization (NPSO) and Hybrid Particle Swarm Optimization (HPSO). The EHPSO algorithm applies the principle of best neighbor of each particle to the HPSO algorithm. Simulation results indicate that EHPSO outperforms HPSO and NPSO in evaluating accurate node positions and improves convergence by avoiding being trapped into local optima.

Downloads

Published

2018-06-22

How to Cite

Li, D., Cheng, D., Qin, J., Liu, S., & Liu, P. (2018). EHPSO: An Enhanced Hybrid Particle Swarm Optimization Algorithm for Internet of Things. International Journal of Online and Biomedical Engineering (iJOE), 14(06), pp. 203–211. https://doi.org/10.3991/ijoe.v14i06.8305

Issue

Section

Short Papers