ArticleBiomimetics (Basel, Switzerland)2025
MHO: A Modified Hippopotamus Optimization Algorithm for Global Optimization and Engineering Design Problems.
Article in Biomimetics (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- An enhanced connected banking system optimizer with multiple strategies for numerical optimization problems.Scientific reports · 2026Article
- An enhanced connected banking system optimizer incorporating triple mechanism for solving global optimization problems.Scientific reports · 2026Article
- Ship Rolling Bearing Fault Identification Under Complex Operating Conditions: Multi-Domain Feature Extraction-Based LCM-HO Enhanced LSSVM Approach.Sensors (Basel, Switzerland) · 2025Article
- A Solution Method for Non-Linear Underdetermined Equation Systems in Grounding Grid Corrosion Diagnosis Based on an Enhanced Hippopotamus Optimization Algorithm.Biomimetics (Basel, Switzerland) · 2025Article
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5 authors.
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Abstract
The hippopotamus optimization algorithm (HO) is a novel metaheuristic algorithm that solves optimization problems by simulating the behavior of hippopotamuses. However, the traditional HO algorithm may encounter performance degradation and fall into local optima when dealing with complex global optimization and engineering design problems. In order to solve these problems, this paper proposes a modified hippopotamus optimization algorithm (MHO) to enhance the convergence speed and solution accuracy of the HO algorithm by introducing a sine chaotic map to initialize the population, changing the convergence factor in the growth mechanism, and incorporating the small-hole imaging reverse learning strategy. The MHO algorithm is tested on 23 benchmark functions and successfully solves three engineering design problems. According to the experimental data, the MHO algorithm obtains optimal performance on 13 of these functions and three design problems, exits the local optimum faster, and has better ordering and stability than the other nine metaheuristics. This study proposes the MHO algorithm, which offers fresh insights into practical engineering problems and parameter optimization.
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