A Compact Cat Swarm Optimization Algorithm Based on Small Sample Probability Model
In this paper, a compact cat swarm optimization algorithm based on a Small Sample Probability Model (SSPCCSO) is proposed. In the same way as with previous algorithms, there is a tracking mode and a searching mode in the processing of searching for optimal solutions, but besides these, a novel diffe...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/16/8209 |
| _version_ | 1797411297037385728 |
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| author | Zeyu He Ming Zhao Tie Luo Yimin Yang |
| author_facet | Zeyu He Ming Zhao Tie Luo Yimin Yang |
| author_sort | Zeyu He |
| collection | DOAJ |
| description | In this paper, a compact cat swarm optimization algorithm based on a Small Sample Probability Model (SSPCCSO) is proposed. In the same way as with previous algorithms, there is a tracking mode and a searching mode in the processing of searching for optimal solutions, but besides these, a novel differential operator is introduced in the searching mode, and it is proved that this could greatly enhance the search ability for the potential global best solution. Another highlight of this algorithm is that the gradient descent method is adopted to increase the convergence velocity and reduce the computation cost. More importantly, a small sample probability model is designed to represent the population of samples instead of the normal probability distribution. This representation method could run with low computing power of the equipment, and the whole algorithm only uses a cat with no historical position and velocity. Therefore, it is suitable for solving optimization problems with limited hardware. In the experiment, SSPCCSO is superior to other compact evolutionary algorithms in most benchmark functions and can also perform well compared to some population-based evolutionary algorithms. It provides a new means of solving small sample optimization problems. |
| first_indexed | 2024-03-09T04:44:02Z |
| format | Article |
| id | doaj.art-78e849cea80e48689f20c03341b3b5c4 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T04:44:02Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-78e849cea80e48689f20c03341b3b5c42023-12-03T13:17:55ZengMDPI AGApplied Sciences2076-34172022-08-011216820910.3390/app12168209A Compact Cat Swarm Optimization Algorithm Based on Small Sample Probability ModelZeyu He0Ming Zhao1Tie Luo2Yimin Yang3School of Computer Science, Yangtze University, Jingzhou 434025, ChinaSchool of Computer Science, Yangtze University, Jingzhou 434025, ChinaSchool of Computer Science, Yangtze University, Jingzhou 434025, ChinaSchool of Computer Science, Yangtze University, Jingzhou 434025, ChinaIn this paper, a compact cat swarm optimization algorithm based on a Small Sample Probability Model (SSPCCSO) is proposed. In the same way as with previous algorithms, there is a tracking mode and a searching mode in the processing of searching for optimal solutions, but besides these, a novel differential operator is introduced in the searching mode, and it is proved that this could greatly enhance the search ability for the potential global best solution. Another highlight of this algorithm is that the gradient descent method is adopted to increase the convergence velocity and reduce the computation cost. More importantly, a small sample probability model is designed to represent the population of samples instead of the normal probability distribution. This representation method could run with low computing power of the equipment, and the whole algorithm only uses a cat with no historical position and velocity. Therefore, it is suitable for solving optimization problems with limited hardware. In the experiment, SSPCCSO is superior to other compact evolutionary algorithms in most benchmark functions and can also perform well compared to some population-based evolutionary algorithms. It provides a new means of solving small sample optimization problems.https://www.mdpi.com/2076-3417/12/16/8209compact cat swarm optimizationdifferential operatorsmall samples probability modelgradient descent method |
| spellingShingle | Zeyu He Ming Zhao Tie Luo Yimin Yang A Compact Cat Swarm Optimization Algorithm Based on Small Sample Probability Model Applied Sciences compact cat swarm optimization differential operator small samples probability model gradient descent method |
| title | A Compact Cat Swarm Optimization Algorithm Based on Small Sample Probability Model |
| title_full | A Compact Cat Swarm Optimization Algorithm Based on Small Sample Probability Model |
| title_fullStr | A Compact Cat Swarm Optimization Algorithm Based on Small Sample Probability Model |
| title_full_unstemmed | A Compact Cat Swarm Optimization Algorithm Based on Small Sample Probability Model |
| title_short | A Compact Cat Swarm Optimization Algorithm Based on Small Sample Probability Model |
| title_sort | compact cat swarm optimization algorithm based on small sample probability model |
| topic | compact cat swarm optimization differential operator small samples probability model gradient descent method |
| url | https://www.mdpi.com/2076-3417/12/16/8209 |
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