On the application of nature-inspired grey wolf optimizer algorithm in geodesy

Abstract

<jats:title>Abstract</jats:title><jats:p>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.</jats:p>

L1-norm optimization of problems with arbitrary column rank by Whale method and its improved algorithm for outlier detection

“…Nevertheless, the Whale algorithm or similar contributions such as Yetkin and Berber (2014) and Yetkin and Bilginer (2020) cannot solve rank deficient problems such as GPS networks. In this type of models, the column rank of design matrix is lee than the number of its columns and the problem is underdetermined.…”