###### Abstract
algorithms comes from collective behaviours observed in nature, with the most famous algorithm being particle swarm optimization (PSO) [6]. There are some other algorithms, such as the firefly algorithm (FA) [7], the whale optimization algorithm (WOA) [8], the artificial fish swarm algorithm (AFSA) [9], the artificial bee colony (ABC) [10], the cuckoo search (CS) algorithm [11], the virus colony search (VCS) [12], the Harris hawks optimization (HHO) [13]. Additionally, the artificial rabbit optimization algorithm (ARO) [14] utilizes real rabbits' foraging and hiding strategies and transitions between the two strategies through energy contraction. Algorithms based on physics/chemistry are proposed by observing physical or chemical phenomena in nature. Examples include the simulated annealing algorithm (SA) [15], the gravitational search algorithm (GSA) [16], and the artificial chemical reaction optimization algorithm (ACROA) [17]. There are also other algorithms, such as the water cycle algorithm (WCA) [18], the collision-based optimization (CBO) [19], the galaxy-based search algorithm (GbSA) [20], the Henry gas solubility optimization algorithm (HGSO) [21], the charged system search algorithm (CSS) [22], and so on. Algorithms based on humans are developed by utilizing various characteristics related to humans, such as social, emotional, and cultural aspects. These algorithms include the teaching-learning-based optimization algorithm (TLBO) [23], the social and cultural algorithm (SCA) [24], the focus group (FG) algorithm [25], the poor rich optimization algorithm (PRO) [26], the league championship algorithm (LCA) [27], the student psychology-based optimization (SPBO) [28], the tug of war optimization algorithm (TOW) [29], and the human mind search (HMS) [30]. Of course, some optimization algorithms belong to something other than the above four categories due to different sources of inspiration, such as mathematics, economics, and music. These include the stochastic paint optimization (SPO) [31], the supply-demand-based optimization (SDO) [32], the imperialist competitive algorithm (ICA) [33], the soccer league competition algorithm (SLC) [34], and so on. In this paper, we propose a new swarm intelligence optimization algorithm: the Oryctolagus cuniculus algorithm (OCA), which is inspired by the group behaviour of Oryctolagus cuniculi. We believe that learning from the behaviour of animals in nature can help us design more innovative and efficient optimization algorithms. By Oryctolagus cuniculi avoiding predators' killing through caves and looking for the safest and most grass-rich location, OCA can quickly find the global optimal solution in the complex search space. We propose a new swarm intelligence optimization algorithm, OCA, which is applied to celestial velocimetry navigation. When dealing with complex issues, traditional optimization algorithms face problems such as dimensional disasters, local optima, and slow convergence. At the same time, OCA has better global search and adaptability, and can effectively find global optimal solutions. In the Oryctolagus cuniculus algorithm, the specific location of each Oryctolagus cuniculus is regarded as a solution. We use fitness values to measure the quality of each position. In this paper, the smaller the danger index is, the smaller the error of velocity estimation is, and the better the effect is. Oryctolagus cuniculi dig many connected caves to avoid natural enemies and to find the safest place. By controlling and changing the population size of each zone in the algorithm, the comparison between new and old rabbit kings is updated, improving algorithm efficiency, and providing new ideas and methods for solving complex problems. Compared with the artificial rabbit optimization algorithm (ARO) and the sparrow search algorithm (SSA) [35], the cave Oryctolagus cuniculus algorithm takes the position of each Oryctolagus cuniculus as the measurement standard instead of the energy of each rabbit as the fitness function.