引用本文:董浩铭,姚立忠,王凌,殷涛,罗海军.融合自适应更新参考点策略与种群预测机制的多目标优化算法[J].控制理论与应用,2025,42(11):2136~2146.[点击复制]
DONG Hao-ming,YAO Li-zhong,WANG Ling,YIN Tao,LUO Hai-jun.Multi-objective optimization with adaptive reference-point updates and population prediction[J].Control Theory & Applications,2025,42(11):2136~2146.[点击复制]
融合自适应更新参考点策略与种群预测机制的多目标优化算法
Multi-objective optimization with adaptive reference-point updates and population prediction
摘要点击 368  全文点击 63  投稿时间：2025-04-06  修订日期：2025-10-14
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DOI编号  10.7641/CTA.2025.50141
  2025,42(11):2136-2146
中文关键词  多目标优化  环境适应性  种群预测  自适应参考点更新  铝电解
英文关键词  multi-objective optimization  environmental adaptability  population prediction  adaptive reference point updating  aluminum electrolysis
基金项目  重庆市教委科学技术研究项目(KJZD–K202400513),重庆市自然科学基金项目(CSTB2023NSCQ-MSX0537),国家自然科学基金项目(62573076) 资助.
作者单位E-mail
董浩铭 重庆师范大学物理与电子工程学院 donghaoming0605@163.com 
姚立忠* 重庆师范大学物理与电子工程学院 lizhong_yao@cqnu.edu.cn 
王凌 清华大学自动化系  
殷涛 重庆师范大学物理与电子工程学院  
罗海军 重庆师范大学物理与电子工程学院  
中文摘要
      传统多目标优化算法因参考点分布僵化、环境适应性弱与种群多样性衰减,常导致解集分布失衡且收敛效 率不佳.为此,本文提出融合自适应更新参考点策略和种群预测机制的多目标优化算法.首先,设计一种精英基因 引导生殖交叉算子,通过干扰交换和继承三重机制增强全局搜索与多样性;其次,建立种群预测机制,结合正则化回 归与边界扰动预测新解,经误差修正实现历史信息与新种群的动态融合;然后,提出自适应更新参考点策略,动态剔 除无效点并生成新点,优化高维目标空间覆盖;最后,给出基于自适应更新参考点策略和预测种群机制的多目标优 化算法的完整框架.实验结果表明,该算法在一系列测试问题和实际铝电解工艺参数优化案例中表现优异.
英文摘要
      Traditional multi-objective optimization algorithms often suffer from rigid reference point distribution, weak environmental adaptability, and population diversity degradation, leading to imbalanced solution set distribution and low convergence efficiency. This paper proposes multi-objective optimization with adaptive reference-point updates and pop ulation prediction. Firstly, an elite gene-guided reproductive crossover operator is designed to enhance global search and diversity through a triple mechanism of interference, exchange, and inheritance. Secondly, a population prediction inte grates regularized regression with boundary perturbation to forecast new solutions, achieving dynamic fusion of historical information and new populations via error correction. Thirdly, an adaptive reference point update strategy dynamically eliminates invalid points and generates new ones to improve coverage in high-dimensional objective spaces. Finally, a complete algorithmic framework is established based on these strategies. Experimental results demonstrate the algorithm’s superior performance on benchmark test problems and a real-world aluminum electrolysis process parameter optimization case.