On the application of nature-inspired grey wolf optimizer algorithm in geodesy
Nowadays, solving hard optimization problems using metaheuristic algorithms has attracted bountiful attention. Generally, these algorithms are inspired by natural metaphors. A novel metaheuristic algorithm, namely Grey Wolf Optimization (GWO), might be applied in the solution of geodetic optimizatio...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2020-06-01
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| Series: | Journal of Geodetic Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jogs-2020-0107 |
| _version_ | 1811212582143393792 |
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| author | Yetkin M. Bilginer O. |
| author_facet | Yetkin M. Bilginer O. |
| author_sort | Yetkin M. |
| collection | DOAJ |
| description | Nowadays, solving hard optimization problems using metaheuristic algorithms has attracted bountiful attention. Generally, these algorithms are inspired by natural metaphors. A novel metaheuristic algorithm, namely Grey Wolf Optimization (GWO), might be applied in the solution of geodetic optimization problems. The GWO algorithm is based on the intelligent behaviors of grey wolves and a population based stochastic optimization method. One great advantage of GWO is that there are fewer control parameters to adjust. The algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. In the present paper, the GWO algorithm is applied in the calibration of an Electronic Distance Measurement (EDM) instrument using the Least Squares (LS) principle for the first time. Furthermore, a robust parameter estimator called the Least Trimmed Absolute Value (LTAV) is applied to a leveling network for the first time. The GWO algorithm is used as a computing tool in the implementation of robust estimation. The results obtained by GWO are compared with the results of the ordinary LS method. The results reveal that the use of GWO may provide efficient results compared to the classical approach. |
| first_indexed | 2024-04-12T05:31:28Z |
| format | Article |
| id | doaj.art-a75151eb087540bdb60f506ad36fc035 |
| institution | Directory Open Access Journal |
| issn | 2081-9943 |
| language | English |
| last_indexed | 2024-04-12T05:31:28Z |
| publishDate | 2020-06-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Geodetic Science |
| spelling | doaj.art-a75151eb087540bdb60f506ad36fc0352022-12-22T03:46:04ZengDe GruyterJournal of Geodetic Science2081-99432020-06-01101485210.1515/jogs-2020-0107jogs-2020-0107On the application of nature-inspired grey wolf optimizer algorithm in geodesyYetkin M.0Bilginer O.1Department of Geomatics Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, Izmir, TurkeyDepartment of Geomatics Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, Izmir, TurkeyNowadays, solving hard optimization problems using metaheuristic algorithms has attracted bountiful attention. Generally, these algorithms are inspired by natural metaphors. A novel metaheuristic algorithm, namely Grey Wolf Optimization (GWO), might be applied in the solution of geodetic optimization problems. The GWO algorithm is based on the intelligent behaviors of grey wolves and a population based stochastic optimization method. One great advantage of GWO is that there are fewer control parameters to adjust. The algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. In the present paper, the GWO algorithm is applied in the calibration of an Electronic Distance Measurement (EDM) instrument using the Least Squares (LS) principle for the first time. Furthermore, a robust parameter estimator called the Least Trimmed Absolute Value (LTAV) is applied to a leveling network for the first time. The GWO algorithm is used as a computing tool in the implementation of robust estimation. The results obtained by GWO are compared with the results of the ordinary LS method. The results reveal that the use of GWO may provide efficient results compared to the classical approach.https://doi.org/10.1515/jogs-2020-0107calibrationleast trimmed absolute value estimatornatural computingstochastic optimizationswarm intelligence |
| spellingShingle | Yetkin M. Bilginer O. On the application of nature-inspired grey wolf optimizer algorithm in geodesy Journal of Geodetic Science calibration least trimmed absolute value estimator natural computing stochastic optimization swarm intelligence |
| title | On the application of nature-inspired grey wolf optimizer algorithm in geodesy |
| title_full | On the application of nature-inspired grey wolf optimizer algorithm in geodesy |
| title_fullStr | On the application of nature-inspired grey wolf optimizer algorithm in geodesy |
| title_full_unstemmed | On the application of nature-inspired grey wolf optimizer algorithm in geodesy |
| title_short | On the application of nature-inspired grey wolf optimizer algorithm in geodesy |
| title_sort | on the application of nature inspired grey wolf optimizer algorithm in geodesy |
| topic | calibration least trimmed absolute value estimator natural computing stochastic optimization swarm intelligence |
| url | https://doi.org/10.1515/jogs-2020-0107 |
| work_keys_str_mv | AT yetkinm ontheapplicationofnatureinspiredgreywolfoptimizeralgorithmingeodesy AT bilginero ontheapplicationofnatureinspiredgreywolfoptimizeralgorithmingeodesy |