This research introduces the Multi-Objective Hippopotamus Optimizer (MOHO), a novel meta-heuristic optimization algorithm inspired by the natural behavior of hippos. The algorithm is designed to tackle complex structural optimization problems, particularly those involving truss structures. The study applies MOHO to five well-known truss structures, aiming to minimize structural mass and maximum nodal displacement while adhering to stress and area constraints. The performance of MOHO is compared with six other popular optimization algorithms using four industry-standard performance metrics: Hypervolume Index (HV), Generational Distance (GD), Inverted Generational Difference (IGD), and Spacing-to-Extent (STE). The results, obtained through rigorous testing and statistical analysis, show that MOHO outperforms other algorithms in terms of convergence, diversity, and solution quality. MOHO demonstrates superior exploration and exploitation capabilities, effectively navigating the vast solution space and generating high-quality Pareto-optimal solutions. The study concludes that MOHO is a promising method for solving complex multi-objective structural optimization problems, with potential for further research and application in higher-dimensional engineering challenges.This research introduces the Multi-Objective Hippopotamus Optimizer (MOHO), a novel meta-heuristic optimization algorithm inspired by the natural behavior of hippos. The algorithm is designed to tackle complex structural optimization problems, particularly those involving truss structures. The study applies MOHO to five well-known truss structures, aiming to minimize structural mass and maximum nodal displacement while adhering to stress and area constraints. The performance of MOHO is compared with six other popular optimization algorithms using four industry-standard performance metrics: Hypervolume Index (HV), Generational Distance (GD), Inverted Generational Difference (IGD), and Spacing-to-Extent (STE). The results, obtained through rigorous testing and statistical analysis, show that MOHO outperforms other algorithms in terms of convergence, diversity, and solution quality. MOHO demonstrates superior exploration and exploitation capabilities, effectively navigating the vast solution space and generating high-quality Pareto-optimal solutions. The study concludes that MOHO is a promising method for solving complex multi-objective structural optimization problems, with potential for further research and application in higher-dimensional engineering challenges.