An Improvement of a Mapping Method Based on Ant Colony Algorithm Applied to Smart Cities
The ant colony algorithm has been widely used in the field of data analysis of smart cities. However, the research of the traditional ant colony algorithm is more focused on one-to-one scenarios and there is insufficient research on many-to-one scenarios. Therefore, for the many-to-one topology mapp...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/22/11814 |
| _version_ | 1797465912318623744 |
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| author | Kaiming Xu Jianjun Wu Tengchao Huang Lei Liang |
| author_facet | Kaiming Xu Jianjun Wu Tengchao Huang Lei Liang |
| author_sort | Kaiming Xu |
| collection | DOAJ |
| description | The ant colony algorithm has been widely used in the field of data analysis of smart cities. However, the research of the traditional ant colony algorithm is more focused on one-to-one scenarios and there is insufficient research on many-to-one scenarios. Therefore, for the many-to-one topology mapping problem, this paper proposes a mapping method based on the ant colony algorithm. The design purpose of the mapping algorithm is to study the optimal mapping scheme, which can effectively reduce the cost of solving the problem. The core of the mapping algorithm is to design the objective function of the algorithm optimization. The commonly used optimization objective function and evaluation index is the average hop count; the average hop count is the most important indicator to measure the entire system. The smaller the average hop count, the less the pulse data needs to be forwarded, which can reduce the communication pressure of the system, reduce congestion, reduce the energy consumption caused by communication, and reduce the delay from the generation of pulse data to the response, etc. Therefore, this paper chooses the average hop count as the optimization objective and reduces the average hop count by designing a mapping algorithm. Through the simulation and verification of the improved ant colony algorithm in the scenario of many-to-one topology mapping, it is concluded that the final convergence result and convergence speed of the improved ant colony algorithm are significantly better than those of the traditional ant colony algorithm. |
| first_indexed | 2024-03-09T18:29:15Z |
| format | Article |
| id | doaj.art-55a47e22ce034739ba9c7a2cd98995a2 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T18:29:15Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-55a47e22ce034739ba9c7a2cd98995a22023-11-24T07:41:21ZengMDPI AGApplied Sciences2076-34172022-11-0112221181410.3390/app122211814An Improvement of a Mapping Method Based on Ant Colony Algorithm Applied to Smart CitiesKaiming Xu0Jianjun Wu1Tengchao Huang2Lei Liang3Low Speed Aerodynamics Institute of China Aerodynamics Research and Development Center, Mianyang 621000, ChinaState Key Laboratory of Advanced Optical Communication Systems and Networks, Peking University, Beijing 100871, ChinaState Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310027, ChinaLow Speed Aerodynamics Institute of China Aerodynamics Research and Development Center, Mianyang 621000, ChinaThe ant colony algorithm has been widely used in the field of data analysis of smart cities. However, the research of the traditional ant colony algorithm is more focused on one-to-one scenarios and there is insufficient research on many-to-one scenarios. Therefore, for the many-to-one topology mapping problem, this paper proposes a mapping method based on the ant colony algorithm. The design purpose of the mapping algorithm is to study the optimal mapping scheme, which can effectively reduce the cost of solving the problem. The core of the mapping algorithm is to design the objective function of the algorithm optimization. The commonly used optimization objective function and evaluation index is the average hop count; the average hop count is the most important indicator to measure the entire system. The smaller the average hop count, the less the pulse data needs to be forwarded, which can reduce the communication pressure of the system, reduce congestion, reduce the energy consumption caused by communication, and reduce the delay from the generation of pulse data to the response, etc. Therefore, this paper chooses the average hop count as the optimization objective and reduces the average hop count by designing a mapping algorithm. Through the simulation and verification of the improved ant colony algorithm in the scenario of many-to-one topology mapping, it is concluded that the final convergence result and convergence speed of the improved ant colony algorithm are significantly better than those of the traditional ant colony algorithm.https://www.mdpi.com/2076-3417/12/22/11814ant colony algorithmmany-to-one topologyaverage hop count |
| spellingShingle | Kaiming Xu Jianjun Wu Tengchao Huang Lei Liang An Improvement of a Mapping Method Based on Ant Colony Algorithm Applied to Smart Cities Applied Sciences ant colony algorithm many-to-one topology average hop count |
| title | An Improvement of a Mapping Method Based on Ant Colony Algorithm Applied to Smart Cities |
| title_full | An Improvement of a Mapping Method Based on Ant Colony Algorithm Applied to Smart Cities |
| title_fullStr | An Improvement of a Mapping Method Based on Ant Colony Algorithm Applied to Smart Cities |
| title_full_unstemmed | An Improvement of a Mapping Method Based on Ant Colony Algorithm Applied to Smart Cities |
| title_short | An Improvement of a Mapping Method Based on Ant Colony Algorithm Applied to Smart Cities |
| title_sort | improvement of a mapping method based on ant colony algorithm applied to smart cities |
| topic | ant colony algorithm many-to-one topology average hop count |
| url | https://www.mdpi.com/2076-3417/12/22/11814 |
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