Numerous engineering problems can be represented as minimax optimization problems, including machine learning, classification, robust optimal control, signal processing, game theory, and more. Typically, minimax problems are considered challenging, especially constrained ones. The recently introduced artificial rabbits optimization (ARO) is inspired by the natural behaviour of rabbits. ARO exhibits robust effectiveness in tackling optimization challenges. Despite its advantages, ARO converges early to local optima, especially in complex or multi-modal optimization problems, and it struggles to balance exploration and exploitation, often leading to premature convergence and reduced accuracy. In this paper, we present a chaotic ARO that employs five maps exhibiting randomization behaviour to refresh candidate solutions. We assess the performance of the suggested CARO by applying it to 46 benchmark functions (25 unconstrained and 21 non-smooth minimax) and 15 constrained test functions with diverse characteristics. We evaluate its performance against six swarm intelligence algorithms. Also, we employ the chaotic maps to ARO and the six compared algorithms, and we perform a non-parametric statistical test, the Friedman test, on all outcomes. The findings show that the proposed algorithm can solve both unconstrained and constrained minimax problems more effectively and efficiently than other swarm intelligence methods.