---

The excavation damaged zone (EDZ) can be defined as a rock zone where the rock properties and conditions have been changed due to the processes related to an excavation. This zone affects the behavior of rock mass surrounding the construction that reduces the stability and safety factor and increase probability of failure of the structure. In this paper, a methodology was examined for computing the creation probability of damaged zone by Latin hypercube sampling based on a feed-forward artificial neural network (ANN) optimized by hybrid particle swarm optimization and genetic algorithm (HPSOGA). The HPSOGA was carried out to decide the initial weights of the neural network. A case study in a test gallery of the Gotvand dam, Iran was carried out and creation probabilities of 0.191 for highly damaged zone (HDZ) and 0.502 for EDZ were obtained.

---

Metaheuristic algorithms mostly consist of some parameters influencing their performance when faced with various optimization problems. Therefore, this paper applies Multi-Stage Parameter Adjustment (MSPA), which employs Extreme Latin Hypercube Sampling (XLHS), Primary Optimizer, and Artificial Neural Networks (ANNs) to a recently developed algorithm called the African Vulture Optimization Algorithm (AVOA) and a well-known one named Particle Swarm Optimization (PSO) for tuning their parameters. The performance of PSO is tested against two engineering and AVOA for two structural optimization problems, and their corresponding results are compared to those of their default versions. The results showed that the employment of MSPA improved the performance of both metaheuristic algorithms in all the considered optimization problems.