摘要
本文针对铝电解工艺制造系统难以卓有成效地提升电流效率、降低直流能耗的多目标优化问题,提出了基于函数型进化算子的NSGA-Ⅱ算法.该方法在系统稳定运转基础上求出满足铝电解增效减耗需求的Pareto非劣解集合;利用拥挤熵排序更新种群,准确预算各级前沿解集分布;引入算术交叉并构造新型α函数交叉算子,减少破坏优秀解集的可能性;再根据高斯柯西变异特性产生与迭代次数相关的扰动,扩大搜索范围和精度;最后使用标准测试函数检测算法性能并用三种对比算法求解铝电解实例.实验结果显示,本文所提算法能获得分布均匀的Pareto最优解集,利于铝电解工厂参考决策,实现提效减耗的目的.
Aiming at the multi-objective optimization problem that it is difficult to effectively improve current efficiency and reduce DC energy consumption in the aluminum electrolysis manufacturing system(AEMS), a functional evolutionary operator-based NSGA-Ⅱ(FEONSGA-Ⅱ) is proposed in this paper. Based on the stable operation of the system, the Pareto non-inferior solution set can be obtained to meet the demands of increasing the efficiency and reducing the consumption of aluminum electrolysis. The crowding entropy is used to update the population, and the distribution of the front solution set at all levels is accurately estimated. To reduce the possibility of destroying the excellent solution set, we introduce arithmetic crossover with a new α-function operator. Then, according to the Gaussian Cauchy variation characteristics, the perturbation related to the number of iterations is generated to expand search ranges and accuracy. Finally, the standard test function is used to detect the performance of the algorithm and three comparison algorithms are applied to solve the aluminum electrolysis example. The experimental results show that the proposed algorithm can obtain the Pareto optimal solution set with uniform distribution, which is conducive to the reference decision of aluminum electrolysis plant to achieve the purpose of improving efficiency and reducing consumption.
作者
范倩
龙伟
姚立忠
李炎炎
FAN Qian;LONG Wei;YAO Li-Zhong;LI Yan-Yan(School of Mechanical Engineering,Sichuan University,Chengdu 610065,China;School of Electrical Engineering,Chongqing University of Science&Technology,Chongqing 401331,China)
出处
《四川大学学报（自然科学版）》
CAS
CSCD
北大核心
2021年第6期84-92,共9页
Journal of Sichuan University(Natural Science Edition)
基金
国家自然科学基金(51805059)。
关键词
铝电解工艺
函数型进化算子
NSGA-Ⅱ
拥挤熵排序
多目标优化
Aluminium electrolysis process
Functional evolution operator
NSGA-Ⅱ
Crowding entropy sorting
Multi-objective optimization
