A Mahalanobis Surrogate-Assisted Ant Lion Optimization and Its Application in 3D Coverage of Wireless Sensor Networks
Metaheuristic algorithms are widely employed in modern engineering applications because they do not need to have the ability to study the objective function’s features. However, these algorithms may spend minutes to hours or even days to acquire one solution. This paper presents a novel efficient Ma...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/24/5/586 |
| _version_ | 1797500054827696128 |
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| author | Zhi Li Shu-Chuan Chu Jeng-Shyang Pan Pei Hu Xingsi Xue |
| author_facet | Zhi Li Shu-Chuan Chu Jeng-Shyang Pan Pei Hu Xingsi Xue |
| author_sort | Zhi Li |
| collection | DOAJ |
| description | Metaheuristic algorithms are widely employed in modern engineering applications because they do not need to have the ability to study the objective function’s features. However, these algorithms may spend minutes to hours or even days to acquire one solution. This paper presents a novel efficient Mahalanobis sampling surrogate model assisting Ant Lion optimization algorithm to address this problem. For expensive calculation problems, the optimization effect goes even further by using MSAALO. This model includes three surrogate models: the global model, Mahalanobis sampling surrogate model, and local surrogate model. Mahalanobis distance can also exclude the interference correlations of variables. In the Mahalanobis distance sampling model, the distance between each ant and the others could be calculated. Additionally, the algorithm sorts the average length of all ants. Then, the algorithm selects some samples to train the model from these Mahalanobis distance samples. Seven benchmark functions with various characteristics are chosen to testify to the effectiveness of this algorithm. The validation results of seven benchmark functions demonstrate that the algorithm is more competitive than other algorithms. The simulation results based on different radii and nodes show that MSAALO improves the average coverage by 2.122% and 1.718%, respectively. |
| first_indexed | 2024-03-10T03:56:19Z |
| format | Article |
| id | doaj.art-a191ea8a108c4deca411bc6f6539027c |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T03:56:19Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-a191ea8a108c4deca411bc6f6539027c2023-11-23T10:54:14ZengMDPI AGEntropy1099-43002022-04-0124558610.3390/e24050586A Mahalanobis Surrogate-Assisted Ant Lion Optimization and Its Application in 3D Coverage of Wireless Sensor NetworksZhi Li0Shu-Chuan Chu1Jeng-Shyang Pan2Pei Hu3Xingsi Xue4College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaFujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, ChinaMetaheuristic algorithms are widely employed in modern engineering applications because they do not need to have the ability to study the objective function’s features. However, these algorithms may spend minutes to hours or even days to acquire one solution. This paper presents a novel efficient Mahalanobis sampling surrogate model assisting Ant Lion optimization algorithm to address this problem. For expensive calculation problems, the optimization effect goes even further by using MSAALO. This model includes three surrogate models: the global model, Mahalanobis sampling surrogate model, and local surrogate model. Mahalanobis distance can also exclude the interference correlations of variables. In the Mahalanobis distance sampling model, the distance between each ant and the others could be calculated. Additionally, the algorithm sorts the average length of all ants. Then, the algorithm selects some samples to train the model from these Mahalanobis distance samples. Seven benchmark functions with various characteristics are chosen to testify to the effectiveness of this algorithm. The validation results of seven benchmark functions demonstrate that the algorithm is more competitive than other algorithms. The simulation results based on different radii and nodes show that MSAALO improves the average coverage by 2.122% and 1.718%, respectively.https://www.mdpi.com/1099-4300/24/5/586ant lion optimizationsurrogate modelmahalanobis distanceradial basis function network3D coveragewireless sensor networks |
| spellingShingle | Zhi Li Shu-Chuan Chu Jeng-Shyang Pan Pei Hu Xingsi Xue A Mahalanobis Surrogate-Assisted Ant Lion Optimization and Its Application in 3D Coverage of Wireless Sensor Networks Entropy ant lion optimization surrogate model mahalanobis distance radial basis function network 3D coverage wireless sensor networks |
| title | A Mahalanobis Surrogate-Assisted Ant Lion Optimization and Its Application in 3D Coverage of Wireless Sensor Networks |
| title_full | A Mahalanobis Surrogate-Assisted Ant Lion Optimization and Its Application in 3D Coverage of Wireless Sensor Networks |
| title_fullStr | A Mahalanobis Surrogate-Assisted Ant Lion Optimization and Its Application in 3D Coverage of Wireless Sensor Networks |
| title_full_unstemmed | A Mahalanobis Surrogate-Assisted Ant Lion Optimization and Its Application in 3D Coverage of Wireless Sensor Networks |
| title_short | A Mahalanobis Surrogate-Assisted Ant Lion Optimization and Its Application in 3D Coverage of Wireless Sensor Networks |
| title_sort | mahalanobis surrogate assisted ant lion optimization and its application in 3d coverage of wireless sensor networks |
| topic | ant lion optimization surrogate model mahalanobis distance radial basis function network 3D coverage wireless sensor networks |
| url | https://www.mdpi.com/1099-4300/24/5/586 |
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