Optimization in complex spaces requires algorithms capable of adequately balancing exploration and exploitation. This work proposes a hybridization of Ephemeral Collective Intelligence (MA) and Cockroach Infestation (RIO) algorithms by incorporating a fuzzy system that allows switching between the two algorithms according to the needs of the search process. This system dynamically regulates the switching and execution intensity of the algorithms based on iteration and population diversity, allowing MA to focus on refining the most promising regions of the search space, while RIO introduces the necessary diversity to prevent premature convergence. Experimental results obtained on 15 benchmark mathematical functions demonstrate that the hybrid proposal with fuzzy control outperforms MA and RIO individually on 9 of the 15 functions evaluated. As future work, we propose incorporating additional metrics, such as robustness, to increase the accuracy and effectiveness of the fuzzy adapter.