Analysis of slopes using elitist differential evolution algorithm

Stability analyses of slopes have been a challenge for engineers, requiring development of complex numerical models to assess the risk levels and potential hazards. The numerical models involve combination of analysis methods and integrated optimization approaches, which generally induce intense engineering calculations with upscale time complexity. To obtain good and quick solutions, a robust optimization algorithm is necessary, leading to an efficient and reliable stability analysis framework. Within this context, various optimization techniques involving deterministic and metaheuristic approaches were proposed in the past decades. The proposed methods often suffer from convergence issues have time deficiencies, which highlights a necessity of development of an effective optimization algorithm. In this study, a modified version of Differential Evolution (DE) algorithm named Elitist Differential Evolution (EDE) is proposed to solve slope stability analysis problems. To develop a complete analysis framework, EDE is integrated with a non-circular failure surface generation method and limit equilibrium based stability analysis techniques. Its performance is compared with other optimization algorithms such as conventional DE, Particle Swarm Optimization and Grey Wolf Optimizer using benchmark problems reported in the literature. The experiments demonstrate that EDE greatly improves the results of other alternatives, validating the applicability of the algorithm to slope stability analysis. Furthermore, statistical performance of EDE has become prominent in the experiments, which further emphasizes its robustness