The Network Global Optimal Mapping Approach Utilizing a Discrete Firefly Optimization Algorithm
The three methods, agent-based model (ABM), product life cycle management (PLM), and discrete firefly optimization algorithm (DFOA), used herein rely on local infrastructure functions after reviewing the local and global functions. Then, a resolution of the multi-layered neural network is proposed....
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/5486948 |
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| _version_ | 1832549615958753280 |
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| author | He Jia Li Xiaomei |
| author_facet | He Jia Li Xiaomei |
| author_sort | He Jia |
| collection | DOAJ |
| description | The three methods, agent-based model (ABM), product life cycle management (PLM), and discrete firefly optimization algorithm (DFOA), used herein rely on local infrastructure functions after reviewing the local and global functions. Then, a resolution of the multi-layered neural network is proposed. A resolution has been saved at all levels of the structure. A global approximation function that keeps learning samples stored is employed. The local map is converted using a set having a respective free rotation. Then, the translation is reflected by a global map of each local map using the affine transformation. The differences of the conversion that the optimal global map uses by minimizing the common sensor nodes are shared by the discovery of different local maps. The optimal conversion is found by running a discrete firefly optimization algorithm (DFOA). Thus, local map registration can resolve the merged map-based approach for each of several pairs and can achieve better performance. Therefore, it provides a systematic approach to building a global map from a local map. A computer simulation was conducted to verify the performance and efficiency of the algorithm. |
| format | Article |
| id | doaj-art-ce4f025b81344fdfaa392cccd006e439 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-ce4f025b81344fdfaa392cccd006e4392025-02-03T06:10:56ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/5486948The Network Global Optimal Mapping Approach Utilizing a Discrete Firefly Optimization AlgorithmHe Jia0Li Xiaomei1Huaihua UniversityHuaihua UniversityThe three methods, agent-based model (ABM), product life cycle management (PLM), and discrete firefly optimization algorithm (DFOA), used herein rely on local infrastructure functions after reviewing the local and global functions. Then, a resolution of the multi-layered neural network is proposed. A resolution has been saved at all levels of the structure. A global approximation function that keeps learning samples stored is employed. The local map is converted using a set having a respective free rotation. Then, the translation is reflected by a global map of each local map using the affine transformation. The differences of the conversion that the optimal global map uses by minimizing the common sensor nodes are shared by the discovery of different local maps. The optimal conversion is found by running a discrete firefly optimization algorithm (DFOA). Thus, local map registration can resolve the merged map-based approach for each of several pairs and can achieve better performance. Therefore, it provides a systematic approach to building a global map from a local map. A computer simulation was conducted to verify the performance and efficiency of the algorithm.http://dx.doi.org/10.1155/2022/5486948 |
| spellingShingle | He Jia Li Xiaomei The Network Global Optimal Mapping Approach Utilizing a Discrete Firefly Optimization Algorithm Journal of Advanced Transportation |
| title | The Network Global Optimal Mapping Approach Utilizing a Discrete Firefly Optimization Algorithm |
| title_full | The Network Global Optimal Mapping Approach Utilizing a Discrete Firefly Optimization Algorithm |
| title_fullStr | The Network Global Optimal Mapping Approach Utilizing a Discrete Firefly Optimization Algorithm |
| title_full_unstemmed | The Network Global Optimal Mapping Approach Utilizing a Discrete Firefly Optimization Algorithm |
| title_short | The Network Global Optimal Mapping Approach Utilizing a Discrete Firefly Optimization Algorithm |
| title_sort | network global optimal mapping approach utilizing a discrete firefly optimization algorithm |
| url | http://dx.doi.org/10.1155/2022/5486948 |
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