The Golden jackal optimization (GJO) represents a new nature-inspired optimization algorithm. The GJO is inspired by the cooperative hunting tactics that are used by the golden jackals. However, similar to other optimization algorithms, the GJO also encounters issues like becoming trapped in local optima, which hinders its ability to find the best solution. To address this issue, the current study presents a new local search operator based on Levy flight. The best solution at the end of each main loop iteration is improved using the Levy operator, leading to the development of an improved algorithm named the Improved Golden jackal optimization (IGJO). The effectiveness of the IGJO was confirmed through testing on 29 benchmark functions from IEEE CEC-2017. The experimental findings show that the IGJO surpasses the original GJO and other optimization algorithms such as the CSA, ChoA, AOA, and BOA.