Double-Group Particle Swarm Optimization and Its Application in Remote Sensing Image Segmentation
Particle Swarm Optimization (PSO) is a well-known meta-heuristic. It has been widely used in both research and engineering fields. However, the original PSO generally suffers from premature convergence, especially in multimodal problems. In this paper, we propose a double-group PSO (DG-PSO) algorith...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-05-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/5/1393 |
| _version_ | 1798039632477159424 |
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| author | Liang Shen Xiaotao Huang Chongyi Fan |
| author_facet | Liang Shen Xiaotao Huang Chongyi Fan |
| author_sort | Liang Shen |
| collection | DOAJ |
| description | Particle Swarm Optimization (PSO) is a well-known meta-heuristic. It has been widely used in both research and engineering fields. However, the original PSO generally suffers from premature convergence, especially in multimodal problems. In this paper, we propose a double-group PSO (DG-PSO) algorithm to improve the performance. DG-PSO uses a double-group based evolution framework. The individuals are divided into two groups: an advantaged group and a disadvantaged group. The advantaged group works according to the original PSO, while two new strategies are developed for the disadvantaged group. The proposed algorithm is firstly evaluated by comparing it with the other five popular PSO variants and two state-of-the-art meta-heuristics on various benchmark functions. The results demonstrate that DG-PSO shows a remarkable performance in terms of accuracy and stability. Then, we apply DG-PSO to multilevel thresholding for remote sensing image segmentation. The results show that the proposed algorithm outperforms five other popular algorithms in meta-heuristic-based multilevel thresholding, which verifies the effectiveness of the proposed algorithm. |
| first_indexed | 2024-04-11T21:56:33Z |
| format | Article |
| id | doaj.art-350a510a74214e4696c88d672625090e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:56:33Z |
| publishDate | 2018-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-350a510a74214e4696c88d672625090e2022-12-22T04:01:05ZengMDPI AGSensors1424-82202018-05-01185139310.3390/s18051393s18051393Double-Group Particle Swarm Optimization and Its Application in Remote Sensing Image SegmentationLiang Shen0Xiaotao Huang1Chongyi Fan2College of Electronic Science, National University of Defense Technology, Changsha 410000, ChinaCollege of Electronic Science, National University of Defense Technology, Changsha 410000, ChinaCollege of Electronic Science, National University of Defense Technology, Changsha 410000, ChinaParticle Swarm Optimization (PSO) is a well-known meta-heuristic. It has been widely used in both research and engineering fields. However, the original PSO generally suffers from premature convergence, especially in multimodal problems. In this paper, we propose a double-group PSO (DG-PSO) algorithm to improve the performance. DG-PSO uses a double-group based evolution framework. The individuals are divided into two groups: an advantaged group and a disadvantaged group. The advantaged group works according to the original PSO, while two new strategies are developed for the disadvantaged group. The proposed algorithm is firstly evaluated by comparing it with the other five popular PSO variants and two state-of-the-art meta-heuristics on various benchmark functions. The results demonstrate that DG-PSO shows a remarkable performance in terms of accuracy and stability. Then, we apply DG-PSO to multilevel thresholding for remote sensing image segmentation. The results show that the proposed algorithm outperforms five other popular algorithms in meta-heuristic-based multilevel thresholding, which verifies the effectiveness of the proposed algorithm.http://www.mdpi.com/1424-8220/18/5/1393particle swarm optimizationmultilevel thresholdingremote sensing image segmentationmeta-heuristicswarm intelligence |
| spellingShingle | Liang Shen Xiaotao Huang Chongyi Fan Double-Group Particle Swarm Optimization and Its Application in Remote Sensing Image Segmentation Sensors particle swarm optimization multilevel thresholding remote sensing image segmentation meta-heuristic swarm intelligence |
| title | Double-Group Particle Swarm Optimization and Its Application in Remote Sensing Image Segmentation |
| title_full | Double-Group Particle Swarm Optimization and Its Application in Remote Sensing Image Segmentation |
| title_fullStr | Double-Group Particle Swarm Optimization and Its Application in Remote Sensing Image Segmentation |
| title_full_unstemmed | Double-Group Particle Swarm Optimization and Its Application in Remote Sensing Image Segmentation |
| title_short | Double-Group Particle Swarm Optimization and Its Application in Remote Sensing Image Segmentation |
| title_sort | double group particle swarm optimization and its application in remote sensing image segmentation |
| topic | particle swarm optimization multilevel thresholding remote sensing image segmentation meta-heuristic swarm intelligence |
| url | http://www.mdpi.com/1424-8220/18/5/1393 |
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