Enhanced hippopotamus optimization algorithm and artificial neural network for mechanical component design

Abstract

Metaheuristics have evolved as a strong family of optimization algorithms capable of handling complicated real-world problems that are frequently non-linear, non-convex, and multidimensional in character. These algorithms efficiently explore and take advantage of search areas by imitating natural processes. In addition to introducing a unique modified hippopotamus optimization algorithm (MHOA) in conjunction with artificial neural networks (ANN), this research examines the most recent developments in metaheuristics. By utilizing ANN's adaptive learning processes, MHOA improves on the original hippopotamus optimization algorithm (HOA) in terms of convergence and solution quality. The study uses MHOA to solve a number of engineering design optimization issues, such as gearbox weight reduction, robot gripper design, structural optimization, and piston lever design. When compared to more conventional algorithms, MHOA performs better in terms of accuracy, robustness, and convergence time.