scholarly journals An integrated fluvial and flash pluvial model using 2D high-resolution sub-grid and particle swarm optimization-based random forest approaches in GIS

2018 ◽  
Vol 5 (3) ◽  
pp. 283-302 ◽  
Author(s):  
Hossein Mojaddadi Rizeei ◽  
Biswajeet Pradhan ◽  
Maryam Adel Saharkhiz
Keyword(s):  
Particle Swarm Optimization ◽  
High Resolution ◽  
Random Forest ◽  
Particle Swarm ◽  
Swarm Optimization

A hybrid feature selection algorithm combining ReliefF and Particle swarm optimization for high-dimensional medical data

2021 ◽  
pp. 1-15
Author(s):  
Zhaozhao Xu ◽  
Derong Shen ◽  
Yue Kou ◽  
Tiezheng Nie
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Random Forest ◽  
Classification Accuracy ◽  
Particle Swarm ◽  
Medical Data ◽  
High Dimensional ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Swarm Optimization

Due to high-dimensional feature and strong correlation of features, the classification accuracy of medical data is not as good enough as expected. feature selection is a common algorithm to solve this problem, and selects effective features by reducing the dimensionality of high-dimensional data. However, traditional feature selection algorithms have the blindness of threshold setting and the search algorithms are liable to fall into a local optimal solution. Based on it, this paper proposes a hybrid feature selection algorithm combining ReliefF and Particle swarm optimization. The algorithm is mainly divided into three parts: Firstly, the ReliefF is used to calculate the feature weight, and the features are ranked by the weight. Then ranking feature is grouped according to the density equalization, where the density of features in each group is the same. Finally, the Particle Swarm Optimization algorithm is used to search the ranking feature groups, and the feature selection is performed according to a new fitness function. Experimental results show that the random forest has the highest classification accuracy on the features selected. More importantly, it has the least number of features. In addition, experimental results on 2 medical datasets show that the average accuracy of random forest reaches 90.20%, which proves that the hybrid algorithm has a certain application value.

scholarly journals Video Superresolution via Parameter-Optimized Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yunyi Yan ◽  
Yujie He ◽  
Yingying Hu ◽  
Baolong Guo
Keyword(s):  
Particle Swarm Optimization ◽  
High Resolution ◽  
Video Sequence ◽  
Signal To Noise Ratio ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Objective Standard ◽  
Objective Performance ◽  
Multiple Swarms

Video superresolution (VSR) aims to reconstruct a high-resolution video sequence from a low-resolution sequence. We propose a novel particle swarm optimization algorithm named as parameter-optimized multiple swarms PSO (POMS-PSO). We assessed the optimization performance of POMS-PSO by four standard benchmark functions. To reconstruct high-resolution video, we build an imaging degradation model. In view of optimization, VSR is converted to an optimization computation problem. And we take POMS-PSO as an optimization method to solve the VSR problem, which overcomes the poor effect, low accuracy, and large calculation cost in other VSR algorithms. The proposed VSR method does not require exact movement estimation and does not need the computation of movement vectors. In terms of peak signal-to-noise ratio (PSNR), sharpness, and entropy, the proposed VSR method based POMS-PSO showed better objective performance. Besides objective standard, experimental results also proved the proposed method could reconstruct high-resolution video sequence with better subjective quality.

scholarly journals Refocusing High-Resolution SAR Images of Complex Moving Vessels Using Co-Evolutionary Particle Swarm Optimization

2020 ◽  
Vol 12 (20) ◽  
pp. 3302
Author(s):  
Lei Yu ◽  
Chunsheng Li ◽  
Jie Chen ◽  
Pengbo Wang ◽  
Zhirong Men
Keyword(s):  
Particle Swarm Optimization ◽  
High Resolution ◽  
Global Convergence ◽  
Particle Swarm ◽  
Sar Images ◽  
Processing Efficiency ◽  
Swarm Optimization ◽  
Time Frequency ◽  
Imaging Results ◽  
Phase Signal

To increase the global convergence and processing efficiency of particle swarm optimization (PSO) applied in the adaptive joint time-frequency, in this study an improved PSO is proposed to refocus the high-resolution SAR images of complex moving vessels in high sea states. According to the characteristics of the high-order multi-component polynomial phase signal, this algorithm provides parallel processing and co-evolution methods by setting the different permissions of the sub-population and sharing its search information. As a result, the multiple components can be extracted simultaneously. Experiments were conducted using the simulation data and Gaofen-3 (GF-3) SAR data. Results showed the processing speed increased by more than 40% and the global convergence was significantly improved. The imaging results verify the efficiency and robustness of this co-evolutionary PSO.

Sign in / Sign up

Export Citation Format

Share Document