GPS Position Prediction Method Based on Chaotic Map-Based Flower Pollination Algorithm
GPS position data prediction can effectively alleviate urban traffic, population flow, route planning, etc. It has very important research significance. Using swarm intelligence optimization algorithm to predict geographic location has important research strategies. Flower pollination algorithm (FPA...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/9972701 |
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| _version_ | 1832554573197213696 |
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| author | Wanjun Yang Zengwu Sun |
| author_facet | Wanjun Yang Zengwu Sun |
| author_sort | Wanjun Yang |
| collection | DOAJ |
| description | GPS position data prediction can effectively alleviate urban traffic, population flow, route planning, etc. It has very important research significance. Using swarm intelligence optimization algorithm to predict geographic location has important research strategies. Flower pollination algorithm (FPA) is a new swarm intelligence optimization algorithm (SIOA) and easy to implement and has other characteristics; more and more scholars have continuously improved it and applied it to more fields. Aiming at the fact that FPA leads to the local optimal value in cross-pollination, the chaotic mapping strategy is proposed to optimize related issues that the population is not rich enough in the self-pollination process. The improved flower pollination algorithm has better advantages in testing function convergence and geographic location prediction effect. |
| format | Article |
| id | doaj-art-764d14940876478c87a17ad13c940501 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-764d14940876478c87a17ad13c9405012025-02-03T05:51:12ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/99727019972701GPS Position Prediction Method Based on Chaotic Map-Based Flower Pollination AlgorithmWanjun Yang0Zengwu Sun1Chongqing Key Laboratory of Spatial Data Mining and Big Data Integration for Ecology and Environment, Chongqing Finance and Economics College, Chongqing 401320, ChinaCollege of Medical Information Engineering, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an 271016, ChinaGPS position data prediction can effectively alleviate urban traffic, population flow, route planning, etc. It has very important research significance. Using swarm intelligence optimization algorithm to predict geographic location has important research strategies. Flower pollination algorithm (FPA) is a new swarm intelligence optimization algorithm (SIOA) and easy to implement and has other characteristics; more and more scholars have continuously improved it and applied it to more fields. Aiming at the fact that FPA leads to the local optimal value in cross-pollination, the chaotic mapping strategy is proposed to optimize related issues that the population is not rich enough in the self-pollination process. The improved flower pollination algorithm has better advantages in testing function convergence and geographic location prediction effect.http://dx.doi.org/10.1155/2021/9972701 |
| spellingShingle | Wanjun Yang Zengwu Sun GPS Position Prediction Method Based on Chaotic Map-Based Flower Pollination Algorithm Complexity |
| title | GPS Position Prediction Method Based on Chaotic Map-Based Flower Pollination Algorithm |
| title_full | GPS Position Prediction Method Based on Chaotic Map-Based Flower Pollination Algorithm |
| title_fullStr | GPS Position Prediction Method Based on Chaotic Map-Based Flower Pollination Algorithm |
| title_full_unstemmed | GPS Position Prediction Method Based on Chaotic Map-Based Flower Pollination Algorithm |
| title_short | GPS Position Prediction Method Based on Chaotic Map-Based Flower Pollination Algorithm |
| title_sort | gps position prediction method based on chaotic map based flower pollination algorithm |
| url | http://dx.doi.org/10.1155/2021/9972701 |
| work_keys_str_mv | AT wanjunyang gpspositionpredictionmethodbasedonchaoticmapbasedflowerpollinationalgorithm AT zengwusun gpspositionpredictionmethodbasedonchaoticmapbasedflowerpollinationalgorithm |