On the Localization Properties of Swarm Intelligence Algorithms

This study explores the use of Artificial Bee Colony (ABC), Differential Evolution (DE), Firefly Algorithm (FA) and Grey Wolf Optimization (GWO) swarm intelligence based optimization methods on four search spaces including Sphere, Rosenbrock's saddle, Rastrigin's and Ackley's function. The results showed that GWO had faster convergence with higher precision and was suitable for simple optimization problems. In contrast, DE optimization required more time to converge but managed to locate the minimum point, making it suitable for complex problems. The application of these optimization methods can have significant benefits in the field of Geographic Information Systems (GIS) and landslides. Overall, the findings of this study provide a basis for further research and exploration of the potential applications of ABC, DE, FA and GWO algorithms in the context of GIS and landslides, which can ultimately improve decision-making efficiency and accuracy in these fields.