This research article introduces the Multi-Objective Hippopotamus Optimizer (MOHO), a novel multi-objective optimization algorithm inspired by the natural behaviors of hippos. The Hippopotamus Optimizer (HO) is a meta-heuristic algorithm that models the behaviors of hippos, including their semi-aquatic lifestyle, herbivorous diet, and defensive strategies against predators. MOHO extends HO to handle multiple objectives, such as minimizing structural mass and reducing maximum nodal displacement in truss structures, while adhering to constraints on stress and area. The study applies MOHO to five well-known truss structures: 10-bar, 25-bar, 60-bar ring, 72-bar, and 942-bar tower trusses. The algorithm is compared with six other multi-objective optimization algorithms, including MOAS, MOACS, DEMO, NSGA-2, MOALO, and MOMFO. Performance metrics such as Hypervolume (HV), Generational Distance (GD), Inverted Generational Difference (IGD), and Spacing-to-Extent (STE) are used to evaluate the algorithms. The results show that MOHO outperforms other algorithms in terms of convergence, diversity, and the quality of Pareto-optimal solutions. MOHO's performance is analyzed using various metrics, including the hypervolume index, which measures the volume of the objective space dominated by the Pareto front. The algorithm's ability to generate diverse and high-quality solutions is demonstrated through Pareto front plots and swarm plots, which show the distribution of solutions across the objective space. The study also highlights the effectiveness of MOHO in exploring the solution space and avoiding premature convergence to local optima. The results indicate that MOHO is a promising algorithm for multi-objective truss design, offering superior performance in terms of convergence, diversity, and solution quality. The algorithm's ability to balance exploration and exploitation is crucial for effectively solving complex optimization problems in structural design. The study concludes that MOHO is a valuable tool for addressing multi-objective optimization challenges in truss design.This research article introduces the Multi-Objective Hippopotamus Optimizer (MOHO), a novel multi-objective optimization algorithm inspired by the natural behaviors of hippos. The Hippopotamus Optimizer (HO) is a meta-heuristic algorithm that models the behaviors of hippos, including their semi-aquatic lifestyle, herbivorous diet, and defensive strategies against predators. MOHO extends HO to handle multiple objectives, such as minimizing structural mass and reducing maximum nodal displacement in truss structures, while adhering to constraints on stress and area. The study applies MOHO to five well-known truss structures: 10-bar, 25-bar, 60-bar ring, 72-bar, and 942-bar tower trusses. The algorithm is compared with six other multi-objective optimization algorithms, including MOAS, MOACS, DEMO, NSGA-2, MOALO, and MOMFO. Performance metrics such as Hypervolume (HV), Generational Distance (GD), Inverted Generational Difference (IGD), and Spacing-to-Extent (STE) are used to evaluate the algorithms. The results show that MOHO outperforms other algorithms in terms of convergence, diversity, and the quality of Pareto-optimal solutions. MOHO's performance is analyzed using various metrics, including the hypervolume index, which measures the volume of the objective space dominated by the Pareto front. The algorithm's ability to generate diverse and high-quality solutions is demonstrated through Pareto front plots and swarm plots, which show the distribution of solutions across the objective space. The study also highlights the effectiveness of MOHO in exploring the solution space and avoiding premature convergence to local optima. The results indicate that MOHO is a promising algorithm for multi-objective truss design, offering superior performance in terms of convergence, diversity, and solution quality. The algorithm's ability to balance exploration and exploitation is crucial for effectively solving complex optimization problems in structural design. The study concludes that MOHO is a valuable tool for addressing multi-objective optimization challenges in truss design.