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This study presents performance evaluation of the Hippopotamus Optimization (HO) algorithm on a shifted dome benchmark function. The effectiveness of HO is evaluated through comparison with several state-of-art of metaheuristic algorithms. All algorithms were executed under identical conditions with 30 Dimension. To ensure statistical reliability, each method was independently run 30 times. Performance was evaluated using best, mean, and standard deviation of objective values, along with convergence behavior. In addition, statistical significance of the results was verified using the Friedman test and Wilcoxon signed-rank test. Experimental results demonstrate that the HO algorithm consistently achieves the global optimum of the dome function with 0.0004 mean error and negligible variance across all runs. The statistical analysis confirms that HO significantly outperforms all competing algorithms at the 5% significance level. These findings indicate that the Hippopotamus Optimization algorithm is a robust and effective method for continuous optimization problems characterized by smooth search landscapes, highlighting its potential for broader real-world applications.