Improvements in the production and energy consumption of the aluminum electrolysis process (AEP) directly depend on the operating parameters of the electrolytic cell. To balance the conflicting goals of efficiency and productivity with reduced energy consumption and emissions, AEP operating parameter optimization is formulated as a constrained multiobjective optimization problem with competing objectives of current efficiency and cell voltage. Then, the improved multiobjective quantum-behaved particle swarm optimization (IMQPSO) algorithm is proposed. The application of an adaptive opposition-based learning strategy and a piecewise Gauss mutation operator can increase the diversity of the population and enhance the global search ability of the IMQPSO. To expand the creativity of the particles, two iterative methods of the mean best position with weighting and the attractor position are redesigned. Experimental analyses are conducted for the benchmark problems and a real case to verify the effectiveness of the proposed method.

中文翻译：

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改进的量子行为粒子群算法优化铝电解过程的运行参数

铝电解工艺（AEP）的生产和能耗的提高直接取决于电解池的运行参数。为了在效率和生产率的矛盾目标与降低的能源消耗和排放之间取得平衡，AEP工作参数优化被公式化为一个约束的多目标优化问题，具有电流效率和电池电压的竞争目标。然后，提出了改进的多目标量子行为粒子群算法（IMQPSO）。自适应的基于对立面的学习策略和分段高斯变异算子的应用可以增加种群的多样性并增强IMQPSO的全局搜索能力。为了扩大粒子的创造力，重新设计了具有权重的平均最佳位置和吸引子位置的两种迭代方法。对基准问题和实际案例进行了实验分析，以验证所提出方法的有效性。