The hippopotamus optimization algorithm (HOA) is a promising novel metaheuristic which shows impressive convergence and solution accuracy. However, its performance can be inadequate for complex optimization problems and real-world applications, particularly due to issues such as local optima and limited search range. To address these challenges, three enhancement strategies are introduced: Cauchy mutation to control search range, developed an elite neighborhood search mechanism to control the diversity, and a trigonometric mutation operator for better exploration. The effectiveness of the Modified Hippopotamus Optimization Algorithm (MHOA) is assessed through comparative simulations over 41 benchmark functions where its superiority over other algorithms was demonstrated. The simulation study confirms that the proposed modifications employed for MHOA impart it with superior optimization capabilities.