A Comparison of Genetic Algorithms and Particle Swarm Optimization to Estimate Cluster-Based Kriging Parameters

Kriging is one of the most used spatial estimation methods in real-world applications. Some kriging parameters must be estimated in order to reach a good accuracy in the interpolation process, however, this task remains a challenge. Various optimization methods have been tested to find good parameters of the kriging process. In recent years, many authors are using bio-inspired techniques and achieving good results in estimating these parameters in comparison with traditional techniques. This paper presents a comparison between well known bio-inspired techniques such as Genetic Algorithms and Particle Swarm Optimization in the estimation of the essential kriging parameters: nugget, sill, range, angle, and factor. In order to perform the tests, we proposed a methodology based on the cluster-based kriging method. Considering the Friedman test, the results showed no statistical difference between the evaluated algorithms in optimizing kriging parameters. On the other hand, the Particle Swarm Optimization approach presented a faster convergence, which is important in this high-cost computational problem.