Multi-objective optimization driven by preponderant individuals and symmetric sampling for operational parameter design in aluminum electrolysis process

Developing advanced population-based multi-objective optimization algorithms to explore optimal operating parameters is a crucial means of achieving energy saving and consumption reduction in aluminum electrolysis process (AEP). Many techniques, such as NSGA-II/NSGA-III, rely on dominance relationships among population members to prioritize evolving individuals. However, this mechanism assumes equal relationships among selected candidates, overlooking the potential of preponderant individuals to guide rapid population evolution. To tackle this issue, we propose a multi-objective optimization algorithm driven by preponderant individuals and symmetric sampling (MOPISS). This algorithm first establishes an individual selection strategy driven by preponderant individuals to expedite population evolution. Subsequently, it employs the count of preponderant individuals to calculate the interaction learning probability (ILP) for adaptively adjusting the genetic intensity of population. ILP is then integrated into the evolutionary process to create a novel genetic mechanism based on symmetric sampling, thereby enhancing the randomness of the solution space and genetic efficacy. Finally, MOPISS is validated across 17 benchmark functions and operational parameter optimization cases of AEP. The results indicate that the proposed method exhibits a significant competitive advantage over other mainstream approaches in terms of evolutionary speed and optimization performance. Specifically, it can increase current efficiency by approximately 3% and reduce energy consumption by 276 kW⋅h/t-Al for AEP.