Abstract: This paper proposes an improved hippo optimization algorithm based on multi-strategy and multi-dimension fusion(MSMDHO) to address various issues of the original hippopotamus optimization algorithm(HO) such as slow convergence speed,susceptibility to local optima,and reliance on algorithm parameters.Firstly,a mapping technique using quasi-reverse learning is employed to generate or perturb the initial population,enhancing the quality of spatial distribution within the population.Secondly,a sine cosine optimization strategy is introduced and applied in the first stage of HO to describe the oscillatory behavior of female or immature hippos in the population positioning formula,using its oscillability to constantly detection and perturbation to achieve better optimization results.Finally,in the defense against predators and fleeing from predators stages of HO,tangent flight strategy and PID search factors are utilized to prevent the population from falling into local optima and improve overall convergence speed.In this paper,the MSMDHO algorithm,HO algorithm,multi-verse optimization(MVO)algorithm,pelican optimization algorithm(POA),rat swarm optimizer(RSO) algorithm,sailfish optimizer(SFO) algorithm,and particle swarm optimization(PSO) algorithm are tested on 8 benchmark functions.Results demonstrate that the MSMDHO algorithm outperforms other algorithms in terms of global search capability,convergence speed stability,and advancement.

Key words: Hippo optimization algorithm, Quasi reverse learning, Sine cosine optimization strategy, Tangent flight, PID search factor