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Water cycle algorithm (WCA) is a new metaheuristic algorithm which the fundamental concepts of WCA are derived from nature and are based on the observation of water cycle process and how rivers and streams flow to sea in the real world. In this paper, the task of sizing optimization of truss structures including discrete and continues variables carried out using WCA, and the optimization results were compared with other well-known optimizers. The obtained statistical results show that the WCA is able to provide faster convergence rate and also manages to achieve better optimal solutions compared to other efficient optimizers.

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The artificial neural network is such a model of biological neural networks containing some of their characteristics and being a member of intelligent dynamic systems. The purpose of applying ANN in civil engineering is their efficiency in some problems that do not have a specific solution or their solution would be very time-consuming. In this study, four different neural networks including FeedForward BackPropagation (FFBP), Radial Basis Function (RBF), Extended Radial Basis Function (ERBF), and Generalized Regression Neural Network (GRNN) have been efficiently trained to analyze large-scale space structures specifically double-layer barrel vaults focusing on their maximum element stresses. To investigate the efficiency of the neural networks, an example has been done and their corresponding results have been compared with their exact amounts obtained by the numerical solution.

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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.