a surrogate model-based aquila optimizer for solving high-dimensional computationally expensive problems

This paper introduces a variant version of the ao for efficiently solving high-dimensional computationally expensive problems. traditional optimization techniques struggle with problems characterized by expensive objective functions and a large number of variables. to address this challenge, this paper proposes a smao that leverages machine learning techniques to approximate the objective function. smao utilizes rbf to build an accurate and efficient surrogate model. by iteratively optimizing the surrogate model, the search process is directed toward the global optimum while significantly reducing the computational cost compared to traditional optimization methods. to evaluate and compare the performance of smao with the surrogate model-based versions of the gazelle optimization algorithm goa, rsa, pdo, and fla, they are analyzed on a set of benchmark test functions with dimensions varying from 30 to 200. according to the reported results, smao has a higher performance compared to others in terms of achieving the nearest solutions to an optimum, early convergence, and accuracy.