Region segmentation of point cloud data based on improved particle swarm optimization fuzzy clustering

2017 ◽  
Vol 25 (4) ◽  
pp. 1095-1105 ◽  
Author(s):  
王晓辉 WANG Xiao-hui ◽  
吴禄慎 WU Lu-shen ◽  
陈华伟 CHEN Hua-wei ◽  
史皓良 SHI Hao-liang
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Clustering ◽  
Point Cloud ◽  
Particle Swarm ◽  
Point Cloud Data ◽  
Swarm Optimization ◽  
Region Segmentation ◽  
Improved Particle Swarm Optimization ◽  
Cloud Data

Hybrid methods for fuzzy clustering based on fuzzy c-means and improved particle swarm optimization

2015 ◽  
Vol 42 (17-18) ◽  
pp. 6315-6328 ◽  
Author(s):  
Telmo M. Silva Filho ◽  
Bruno A. Pimentel ◽  
Renata M.C.R. Souza ◽  
Adriano L.I. Oliveira
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Clustering ◽  
Hybrid Methods ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Fuzzy C Means

Fuzzy Clustering Algorithm Based on Improved Particle Swarm Optimization and its Application

2010 ◽  
Vol 44-47 ◽  
pp. 4067-4071 ◽  
Author(s):  
Xue Yong Li ◽  
Jia Xia Sun ◽  
Jun Hui Fu ◽  
Guo Hong Gao
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Dimensional Space ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Solution Space ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Fuzzy Clustering Algorithm

A fuzzy clustering algorithm based on improved particle swarm optimization was proposed in this paper. First reduce dimension of solution space, separate it into smaller solution space. In separated solution space, use of improved particle swarm optimization algorithm to search the sub-optimal solution as a chromosome of whole particle,use improved PSO to search global optimal solution. The particle solve the problem that swarm algorithm easy to fall into local optimal solution in high dimensional space, and the problem that the fuzzy clustering algorithm is sensitive to initial value problems. Simulation results show the effectiveness of this algorithm.

Compressed Sensing Image Reconstruction Based on Improved Particle Swarm Optimization Algorithm

2014 ◽  
Vol 599-601 ◽  
pp. 1453-1456
Author(s):  
Ju Wang ◽  
Yin Liu ◽  
Wei Juan Zhang ◽  
Kun Li
Keyword(s):  
Particle Swarm Optimization ◽  
Image Reconstruction ◽  
Compressed Sensing ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Matching Pursuit ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The reconstruction algorithm has a hot research in compressed sensing. Matching pursuit algorithm has a huge computational task, when particle swarm optimization has been put forth to find the best atom, but it due to the easy convergence to local minima, so the paper proposed a algorithm ,which based on improved particle swarm optimization. The algorithm referred above combines K-mean and particle swarm optimization algorithm. The algorithm not only effectively prevents the premature convergence, but also improves the K-mean’s local. These findings indicated that the algorithm overcomes premature convergence of particle swarm optimization, and improves the quality of image reconstruction.

Sign in / Sign up

Export Citation Format

Share Document