Hippopotamus Optimization Algorithm: A Novel Nature-Inspired Optimization Algorithm

Abstract

Abstract The novelty of this article lies in introducing a novel nonparametric metaheuristic technique named the Hippopotamus Optimization (HO) algorithm. The HO is conceived by drawing inspiration from the inherent behaviors observed in hippopotamuses, showcasing an innovative approach in metaheuristic methodology. The HO is conceptually defined using a trinary-phase model that incorporates their position updating in rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated. It attained the top rank in 132 out of 161 benchmark functions in finding optimal value, encompassing unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, as well as the CEC 2019 test suite and CEC 2014 test suite dimensions of 10, 30, 50, and 100 and Zigzag Pattern benchmark functions, this suggests that the HO demonstrates a noteworthy proficiency in both local search and exploitation, as well as in global search and exploration. Moreover, it effectively balances exploration and exploitation, supporting the search process. The performance of the HO consistently surpassed that of the top 3 algorithms in achieving optimal value, except for 29 functions. However, although it did not exhibit strong convergence in these 29 functions, the standard deviation for them was lower than the other investigated algorithms, illustrating its ability to manage the functions effectively. In light of the results from addressing four distinct engineering design challenges, the HO has effectively achieved the most efficient resolution while concurrently upholding adherence to the designated constraints. The Wilcoxon signed test demonstrates that HO exhibits a notable and statistically significant advantage over the investigated algorithms in effectively addressing the optimization problems examined in this study.