Name
Abstract | ||
|---|---|---|
Aiming at disadvantages of particle swarm optimization algorithm being easily caught into local extreme point, slow convergence rate of evolution near extreme point, worse precision and others during positioning process, this paper proposes a hybrid annealing particle swarm optimization localization algorithm based on simulated annealing particle swarm algorithm, the idea of “survival of the fittest” is incorporated in the algorithm and the particles with poorer fitness are eliminated according to the Metropolis criterion. At the same time, a minimum positioning error weighting model is proposed to reduce the non-line-of-sight error of anchor nodes during positioning. Simulation and experiment results for several current hybrid particle swarm localization algorithm show that, the localization algorithm can improved the average precision of location and speed up convergence rate. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICACI.2018.8377479 | 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI) |
Keywords | Field | DocType |
indoor positioning systems,particle swarm optimization (PSO),simulated annealing,weighted model,non-line-of-sight,elimination mechanism | Extreme point,Convergence (routing),Simulated annealing,Particle swarm optimization,Weighting,Computer science,Algorithm,Rate of convergence,Wireless sensor network,Speedup | Conference |
ISBN | Citations | PageRank |
978-1-5386-4363-1 | 0 | 0.34 |
References | Authors | |
4 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rentao Zhao | 1 | 0 | 0.34 |
| Yang Shi | 2 | 30 | 8.73 |