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动态多目标优化进化算法研究进展

马永杰 陈敏 龚影 程时升 王甄延

马永杰, 陈敏, 龚影, 程时升, 王甄延. 动态多目标优化进化算法研究进展. 自动化学报, 2020, 46(11): 2302−2318 doi: 10.16383/j.aas.c190489
引用本文: 马永杰, 陈敏, 龚影, 程时升, 王甄延. 动态多目标优化进化算法研究进展. 自动化学报, 2020, 46(11): 2302−2318 doi: 10.16383/j.aas.c190489
Ma Yong-Jie, Chen Min, Gong Ying, Cheng Shi-Sheng, Wang Zhen-Yan. Research progress of dynamic multi-objective optimization evolutionary algorithm. Acta Automatica Sinica, 2020, 46(11): 2302−2318 doi: 10.16383/j.aas.c190489
Citation: Ma Yong-Jie, Chen Min, Gong Ying, Cheng Shi-Sheng, Wang Zhen-Yan. Research progress of dynamic multi-objective optimization evolutionary algorithm. Acta Automatica Sinica, 2020, 46(11): 2302−2318 doi: 10.16383/j.aas.c190489

动态多目标优化进化算法研究进展

doi: 10.16383/j.aas.c190489
基金项目: 国家自然科学基金(62066041, 41861047)资助
详细信息
    作者简介:

    马永杰:西北师范大学物理与电子工程学院电子系教授. 主要研究方向为进化算法. 本文通信作者. E-mail: myjmyj@nwnu.edu.cn

    陈敏:西北师范大学物理与电子工程学院硕士研究生. 主要研究方向为进化算法. E-mail: cm9690@126.com

    龚影:西北师范大学物理与电子工程学院硕士研究生. 主要研究方向为智能计算. E-mail: 15320834175@163.com

    程时升:西北师范大学物理与电子工程学院硕士研究生. 主要研究方向为智能计算. E-mail: shishengcss@163.com

    王甄延:西北师范大学物理与电子工程学院硕士研究生. 主要研究方向为智能计算. E-mail: wzy1136390111@163.com

Research Progress of Dynamic Multi-objective Optimization Evolutionary Algorithm

Funds: Supported by National Natural Science Foundation of China (62066041, 41861047)
  • 摘要: 动态多目标优化问题(Dynamic multi-objective optimization problems, DMOPs)已成为工程优化的研究热点, 其目标函数, 约束函数和相关参数都可能随时间不断变化, 如何利用搜索到的历史最优解对新的环境变化做出快速响应, 是设计动态多目标优化进化算法(Dynamic multi-objective optimization evolutionary algorithm, DMOEA)的重点和难点. 本文在介绍DMOEA的基础上, 分析了近年来基于个体和种群级别的环境响应策略, 多策略混合等的DMOEA主要研究进展, 并介绍了DMOEA的性能测试函数, 评价指标以及在工程优化领域中的应用, 分析了DMOEA研究中仍面临的主要问题, 展望了未来的研究方向.
  • 图  1  DMOEA的设计流程框图

    Fig.  1  The design flow chart of DMOEA

    图  2  基于卡尔曼滤波的预测模型图

    Fig.  2  Relationship of EA with KF model

    图  3  动态环境中多种群调度方法的框架

    Fig.  3  Framework for multi-population methods with scheduling in dynamic environments

    图  4  BSCA算法体系结构图

    Fig.  4  The architecture of BSCA inspired by human NEI systems

    图  5  多种群的粒子群优化框架示意图

    Fig.  5  The framework of multi-swarm particle swarm optimization

    图  6  动态进化环境模型框图

    Fig.  6  A general framework of dynamic environment evolutionary model

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  • 收稿日期:  2019-06-26
  • 录用日期:  2019-11-15
  • 刊出日期:  2020-11-24

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