摘要
作为铜冶炼生产的关键基础,配料计划直接关系到后续过程的连续稳定和企业经济效益.考虑到现有方法给出的配料计划在原料利用率等指标上的不足,本文建立了以最大化资源利用率和配料一致性为目标的铜工业配料计划优化模型,并提出改进的多因子差分进化算法求解.针对基本多因子差分进化算法易早熟收敛、复杂约束导致的大量不可行解等挑战,本文首先设计了基于正余弦算法的变异策略,以兼顾算法跨任务知识迁移与全局搜索和局部开发的能力;其次,引入Levy最优扰动策略,以提高算法在进化后期种群的多样性,避免陷入局部最优.此外,提出具有较低计算成本的不可行解修复策略,保证了算法高效搜索;最后,基于国内某铜业集团真实数据的仿真实验和多任务基准测试集的实验结果证明,与其他方法相比,本文所提模型和方法可显著提高配料计划的资源利用率、配料一致性等指标,可为铜业稳定生产提供良好支撑,并在复杂的多任务基准问题中呈现出良好的稳定性.
As a key foundation of the entire production process of copper products,ingredient plan is directly related to the continuous stability of the subsequent process and the economic benefits of the enterprise.Considering the unsatisfied resource utilization of ingredient plans provided by existing methods,an ingredient optimization model with the objective of maximizing resource utilization and ingredient consistency is established.Then,an improved multifactorial differential evolution algorithm is proposed.To solve premature convergence and infeasible solutions caused by complex constraints in the traditional multifactorial differential evolution algorithm,the improved algorithm first designs a mutation strategy based on the sine cosine algorithm to balance cross task knowledge transfer,global search,and local development capabilities of the algorithm.Secondly,the Levy optimal perturbation strategy is introduced to improve the diversity of solutions,and prevent the algorithm from getting stuck in local optima.In addition,a repair strategy for infeasible solutions with lower computational cost is proposed to ensure search efficiency.Finally,simulation experiments based on real data from a domestic copper industry and multitask benchmark problems demonstrate that compared with other methods,the model and method proposed in this paper can significantly improve the resource utilization rate and consistency of ingredient plan,providing good support for stable production in the copper industry.And the proposed algorithm exhibits good stability in complex multitask benchmark problems.
作者
张雪瑞
韩中洋
赵珺
王伟
ZHANG Xue-rui;HAN Zhong-yangy;ZHAO Jun;WANG Wei(School of Control Science and Engineering,Dalian University of Technology,Dalian Liaoning 116081,China)
出处
《控制理论与应用》
北大核心
2025年第12期2557-2568,共12页
Control Theory & Applications
基金
国家自然科学基金项目(62394345)资助。
关键词
配料优化
进化算法
多任务优化
约束处理
ingredient optimization
evolutionary algorithm
multitask optimization
constraint handling.
