<p>Recent advancements in metaheuristic methods have enhanced the ability to identify global solutions by balancing exploration and exploitation. Among these, the Chernobyl Disaster Optimizer (CDO), introduced in 2023 and inspired by the Chernobyl nuclear disaster, faces challenges with multimodal functions, often becoming trapped in local optima. Similarly, the Grey Wolf Optimizer (GWO), introduced in 2014, is prone to premature convergence and also getting stuck in local optima. Despite these limitations, both algorithms employ strategies to balance exploration and exploitation to mitigate this issue.This study introduces a hybrid approach that combines CDO with an enhanced version of GWO, incorporating a novel operator to integrate the strengths of both methods. In this hybrid method, we propose a non-linear parameter adjustment for CDO and a refined agent propagation strategy to improve search capabilities, alongside a Cauchy-opposite learning operator to increase diversity in the search space. The efficacy of this hybrid approach is rigorously evaluated through quantitative and qualitative analyses, using benchmark functions and the CEC 2019 suite. Experimental results suggest that the hybrid method improves CDO’s ability to avoid local optima, effectively addressing optimization challenges requiring extensive exploration and exploitation. This hybridization represents a meaningful advancement for the CDO framework and demonstrates improved performance in solving selected engineering design problems, with potential for broader application to complex global optimization challenges.</p>