en Optimal design Optimal design پژوهشي Research <span style="font-size:11.5pt"><span style="text-autospace:none"><span new="" roman="" style="font-family:" times="">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.</span></span></span> Metaheuristics, Optimization, Multi-Stage Parameter Adjustment, Artificial Neural Networks, Extreme Latin Hypercube Sampling, AVOA, PSO 259 278 http://ijoce.iust.ac.ir/browse.php?a_code=A-10-6463-27&slc_lang=en&sid=1 A. Kaveh alikaveh@iust.ac.ir 180031947532846002838 180031947532846002838 Yes School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran A. Eskandari amir_eskandari@civileng.iust.ac.ir 180031947532846002839 180031947532846002839 No School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran