基于多目标PSO混合优化的虚拟样本生成

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中文摘要：受限于检测技术难度、高时间与经济成本等原因,难测参数的软测量模型建模样本存在数量少、分布稀疏与不平衡等问题,严重制约了数据驱动模型的泛化性能.针对以上问题,提出一种基于多目标粒子群优化(Multi-objective particle swarm optimization,MOPSO)混合优化的虚拟样本生成(Virtual sample generation,VSG)方法.首先,设计综合学习粒子群优化算法的种群表征机制,使其能够同时编码用于连续变量和离散变量;然后,定义具有多阶段多目标特性的综合学习粒子群优化算法适应度函数,使其能够在确保模型泛化性能的同时最小化虚拟样本数量;最后,提出面向虚拟样本生成的多目标混合优化任务以改进综合学习粒子群优化算法,使其能够适应虚拟样本优选过程的变维特性并提高收敛速度.同时,首次借鉴度量学习提出用于评价虚拟样本质量的综合评价指标和分布相似指标.利用基准数据集和真实工业数据集验证了所提方法的有效性和优越性.

外文标题：Virtual Sample Generation Method Based on Hybrid Optimization With Multi-objective PSO

外文摘要：Due to the difficulty of detection technology,and high time and economic cost,the modeling samples of soft-sensing model with difficult parameters have some problems,such as small numbers,sparse distribution,and imbalance,which seriously restrict the generalization performance of data-driven models.To solve the above prob-lems,a virtual sample generation(VSG)method based on multi-objective particle swarm optimization(MOPSO)hybrid optimization is proposed.First,the population representation mechanism of the integrated learning particle swarm optimization algorithm is designed,so that it can simultaneously encode the continuous and the discrete variables.Then,the fitness function of the integrated learning particle swarm optimization algorithm with multi-stage and multi-objective characteristics is defined to minimize the number of virtual samples while ensuring the generalization performance of the model.Finally,a multi-objective hybrid optimization task is generated for virtual samples to improve the integrated learning particle swarm optimization algorithm,so that it can adapt to the vari-able dimension characteristics of the virtual sample optimization process and improve the convergence speed.At the same time,the comprehensive evaluation index and distribution similarity index are proposed for evaluating the quality of virtual samples by referring to metric learning for the first time.In this paper,two benchmark datasets and an actual industrial dataset are used to verify the effectiveness and superiority of the proposed method.

外文关键词：

Small sample modelingvirtual sample generation(VSG)hybrid optimizationmulti-objective particle swarm optimization(MOPSO)distribution similarity

作者：

王丹丹、汤健、夏恒、乔俊飞

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作者单位：

北京工业大学信息学部北京 100124

北京工业大学智慧环保北京实验室北京 100124

北京工业大学智能感知与自主控制教育部工程研究中心北京 100124

关键词：

小样本建模虚拟样本生成混合优化多目标粒子群优化分布相似度

基金：

国家自然科学基金国家自然科学基金国家自然科学基金北京市自然科学基金北京市自然科学基金科技创新——"新一代人工智能"重大项目(2030)科技创新——"新一代人工智能"重大项目(2030)

项目编号：

620730066217312062021003421203241920092021ZD01123012021ZD0112302

出版年：

2024

DOI：

10.16383/j.aas.c211091

自动化学报

中国自动化学会中国科学院自动化研究所

自动化学报

CSTPCD北大核心

影响因子：1.762

ISSN：0254-4156

年,卷(期)：2024.50(4)

参考文献量56