Collaboratively optimizing operational parameters through leveraging accumulated production experience is an innovative approach to reducing energy consumption in aluminum electrolysis cells (AECs). Due to the dynamic heterogeneity of various AECs, an explicit evolutionary multitasking (EMT) framework capable of incorporating different optimizers, has the potential to tackle this challenge effectively. However, there is a notable gap in theoretical research on multitasking collaborative evolutionary algorithms specifically applied to AECs. Meanwhile, existing explicit EMT algorithms often overlook the intertask correlation of feature information extracted in isolation from individual tasks. These issues significantly limit the development of synergistic effects in multitasking optimization for addressing parameter design in AECs. To address these limitations, this work proposes an explicit evolutionary framework with multitasking feature fusion (EMFF). This framework thoroughly considers the potential connections among feature information from different tasks. It achieves effective knowledge transfer by the design of a unique multitasking feature fusion mechanism, which enhances the information value of source tasks for target tasks. Furthermore, a transfer individual derivation (TID) strategy is introduced to ensure the rapid evolution of critical knowledge. Finally, comprehensive components and designed process are presented. Experimental results demonstrate EMFF's exceptional performance in various benchmark tests and real-world AEC parameter optimization cases.