A Novel Binary QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm
QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm generalized differential evolution (DE) algorithm to matrix form. QUATRE was originally designed for a continuous search space, but many practical applications are binary optimization problems. Therefore, we designed a novel binary version...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/5/2251 |
| _version_ | 1797414844573417472 |
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| author | Shu-Chuan Chu Zhongjie Zhuang Junbao Li Jeng-Shyang Pan |
| author_facet | Shu-Chuan Chu Zhongjie Zhuang Junbao Li Jeng-Shyang Pan |
| author_sort | Shu-Chuan Chu |
| collection | DOAJ |
| description | QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm generalized differential evolution (DE) algorithm to matrix form. QUATRE was originally designed for a continuous search space, but many practical applications are binary optimization problems. Therefore, we designed a novel binary version of QUATRE. The proposed binary algorithm is implemented using two different approaches. In the first approach, the new individuals produced by mutation and crossover operation are binarized. In the second approach, binarization is done after mutation, then cross operation with other individuals is performed. Transfer functions are critical to binarization, so four families of transfer functions are introduced for the proposed algorithm. Then, the analysis is performed and an improved transfer function is proposed. Furthermore, in order to balance exploration and exploitation, a new liner increment scale factor is proposed. Experiments on 23 benchmark functions show that the proposed two approaches are superior to state-of-the-art algorithms. Moreover, we applied it for dimensionality reduction of hyperspectral image (HSI) in order to test the ability of the proposed algorithm to solve practical problems. The experimental results on HSI imply that the proposed methods are better than Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). |
| first_indexed | 2024-03-09T05:39:46Z |
| format | Article |
| id | doaj.art-16b45c1d8343468bb53d652380b90253 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T05:39:46Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-16b45c1d8343468bb53d652380b902532023-12-03T12:25:35ZengMDPI AGApplied Sciences2076-34172021-03-01115225110.3390/app11052251A Novel Binary QUasi-Affine TRansformation Evolutionary (QUATRE) AlgorithmShu-Chuan Chu0Zhongjie Zhuang1Junbao Li2Jeng-Shyang Pan3College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Science and Engineering, Flinders University, 1284 South Road, Clovelly Park, SA 5042, AustraliaSchool of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, ChinaCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaQUasi-Affine TRansformation Evolutionary (QUATRE) algorithm generalized differential evolution (DE) algorithm to matrix form. QUATRE was originally designed for a continuous search space, but many practical applications are binary optimization problems. Therefore, we designed a novel binary version of QUATRE. The proposed binary algorithm is implemented using two different approaches. In the first approach, the new individuals produced by mutation and crossover operation are binarized. In the second approach, binarization is done after mutation, then cross operation with other individuals is performed. Transfer functions are critical to binarization, so four families of transfer functions are introduced for the proposed algorithm. Then, the analysis is performed and an improved transfer function is proposed. Furthermore, in order to balance exploration and exploitation, a new liner increment scale factor is proposed. Experiments on 23 benchmark functions show that the proposed two approaches are superior to state-of-the-art algorithms. Moreover, we applied it for dimensionality reduction of hyperspectral image (HSI) in order to test the ability of the proposed algorithm to solve practical problems. The experimental results on HSI imply that the proposed methods are better than Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).https://www.mdpi.com/2076-3417/11/5/2251binaryQUATREtransfer functiondimension reductionhyperspectral image |
| spellingShingle | Shu-Chuan Chu Zhongjie Zhuang Junbao Li Jeng-Shyang Pan A Novel Binary QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm Applied Sciences binary QUATRE transfer function dimension reduction hyperspectral image |
| title | A Novel Binary QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm |
| title_full | A Novel Binary QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm |
| title_fullStr | A Novel Binary QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm |
| title_full_unstemmed | A Novel Binary QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm |
| title_short | A Novel Binary QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm |
| title_sort | novel binary quasi affine transformation evolutionary quatre algorithm |
| topic | binary QUATRE transfer function dimension reduction hyperspectral image |
| url | https://www.mdpi.com/2076-3417/11/5/2251 |
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