角度修正和分级多种群的动态多目标进化算法

杨乐; 马永杰; 平镐羽; 杨岳

doi:10.12263/DZXB.20221330

您当前的位置：

首页 >

文章列表页 >

角度修正和分级多种群的动态多目标进化算法

学术论文 | 更新时间：2025-12-08

- 角度修正和分级多种群的动态多目标进化算法
- Dynamic Multi-Objective Evolutionary Algorithm Based on Angle Correction and Hierarchical Multi-Population
- 电子学报 2024年52卷第9期页码：3278-3290
- 作者机构：
  
  西北师范大学物理与电子工程学院，甘肃兰州 730070
- 作者简介：
  
  [ "杨乐女，1999年出生于陕西省西安市.现为西北师范大学硕士研究生，主要研究方向为进化计算. E-mail: ylyanglele@163.com" ]
  [ "马永杰男，1967年出生于甘肃省平凉市.西北师范大学教授、博士生导师.主要研究方向为智能计算、图像处理、测控技术.E-mail: myjmyj@nwnu.edu.cn" ]
  [ "平镐羽男，1998年出生于安徽省合肥市.现为西北师范大学硕士研究生，主要研究方向为图像处理、进化计算. E-mail: 2021222472@nwnu.edu.cn" ]
  [ "杨岳男，1997年出生于甘肃省白银市.现为西北师范大学硕士研究生，主要研究方向为进化计算. E-mail: 205752504@qq.com" ]
- 基金信息：
  
  国家自然科学基金(62066041)
- DOI：10.12263/DZXB.20221330
  中图分类号： TP18
- 收稿：2022-11-21，
  
  修回：2023-07-17，
  
  纸质出版：2024-09-25
- 稿件说明：
移动端阅览
杨乐, 马永杰, 平镐羽, 等. 角度修正和分级多种群的动态多目标进化算法[J]. 电子学报, 2024, 52(09): 3278-3290.

YANG Le, MA Yong-jie, PING Hao-yu, et al. Dynamic Multi-Objective Evolutionary Algorithm Based on Angle Correction and Hierarchical Multi-Population[J]. Acta Electronica Sinica, 2024, 52(09): 3278-3290.
杨乐, 马永杰, 平镐羽, 等. 角度修正和分级多种群的动态多目标进化算法[J]. 电子学报, 2024, 52(09): 3278-3290. DOI：10.12263/DZXB.20221330

YANG Le, MA Yong-jie, PING Hao-yu, et al. Dynamic Multi-Objective Evolutionary Algorithm Based on Angle Correction and Hierarchical Multi-Population[J]. Acta Electronica Sinica, 2024, 52(09): 3278-3290. DOI：10.12263/DZXB.20221330

摘要

为更好地应对动态多目标优化中的环境变化，提出了一种对差分向量进行角度修正以及分级多种群协同进化（Angle Correction and Hierarchical Multi-Population， ACHMP）的进化算法.根据历史信息，使用无迹卡尔曼滤波模型来预测种群的中心点，通过不同时刻的中心点产生不同的差分向量，再使用无迹卡尔曼滤波对差分向量进行角度修正；提出的多种群协同进化模式将种群分为三部分并使其沿不同的方向进化，子种群监督主种群进化，在提升了算法性能的同时，也保证了种群的多样性.与10种对比算法在不同测试问题上的实验结果显示，ACHMP算法的性能总体优于其他算法，证明了本文提出的角度修正和分级多种群方法在处理动态多目标优化问题时具有较强的竞争力.

Abstract

In order to better cope with the environmental changes in dynamic multi-objective optimization

an evolutionary algorithm with angular correction of difference vectors and hierarchical multi-population co-evolution (ACHMP) is proposed. According to the historical information

use the unscented Kalman filter model to predict the population centroids

generate different difference vectors through different centroids at different times

and then use the unscented Kalman filter to correct the angle of the difference vectors. A multi-population coevolution model is proposed

which divides the population into three parts to evolve in different directions. The sub-population supervises the evolution of the master population

which not only improves the performance of the algorithm

but also ensures the diversity of the population. Experimental results with 10 comparison algorithms on different test problems show that the ACHMP algorithm performs better than the other algorithms in general

which proves that the angle correction and hierarchical multi-population method proposed in this paper has strong competitiveness in dealing with dynamic multi-objective optimization problems.

关键词

Keywords

references

钟沛龙 , 黎明 , 何超 , 等 . 基于SOM聚类和自适应算子选择的高维多目标进化算法 [J ] . 电子学报 , 2022 , 50 ( 8 ): 1959 - 1974 .

ZHONG P L , LI M , HE C , et al . Many-objective evolutionary algorithm based on som clustering and adaptive operator selection [J ] . Acta Electronica Sinica , 2022 , 50 ( 8 ): 1959 - 1974 . (in Chinese)

马永杰 , 陈敏 , 龚影 , 等 . 动态多目标优化进化算法研究进展 [J ] . 自动化学报 , 2020 , 46 ( 11 ): 2302 - 2318 .

MA Y J , CHEN M , GONG Y , et al . Research progress of dynamic multi-objective optimization evolutionary algorithm [J ] . Acta Automatica Sinica , 2020 , 46 ( 11 ): 2302 - 2318 . (in Chinese)

刘淳安 , 王宇平 . 动态多目标优化的进化算法及其收敛性分析 [J ] . 电子学报 , 2007 , 35 ( 6 ): 1118 - 1121 .

LIU C A , WANG Y P . Evolutionary algorithm for dynamic multi-objective optimization problems and its convergence [J ] . Acta Electronica Sinica , 2007 , 35 ( 6 ): 1118 - 1121 . (in Chinese)

FARINA M , DEB K , AMATO P . Dynamic multiobjective optimization problems: Test cases, approximations, and applications [J ] . IEEE Transactions on Evolutionary Computation , 2004 , 8 ( 5 ): 425 - 442 .

ZHOU A M , JIN Y C , ZHANG Q F . A population prediction strategy for evolutionary dynamic multiobjective optimization [J ] . IEEE Transactions on Cybernetics , 2014 , 44 ( 1 ): 40 - 53 .

AZZOUZ R , BECHIKH S , SAID L BEN . A dynamic multi-objective evolutionary algorithm using a change severity-based adaptive population management strategy [J ] . Soft Computing , 2017 , 21 ( 4 ): 885 - 906 .

YANG Z , JIN Y C , HAO K R . A bio-inspired self-learning coevolutionary dynamic multiobjective optimization algorithm for Internet of Things services [J ] . IEEE Transactions on Evolutionary Computation , 2019 , 23 ( 4 ): 675 - 688 .

LI K , FIALHO A , KWONG S , et al . Adaptive operator selection with bandits for a multiobjective evolutionary algorithm based on decomposition [J ] . IEEE Transactions on Evolutionary Computation , 2014 , 18 ( 1 ): 114 - 130 .

RUAN G , YU G , ZHENG J H , et al . The effect of diversity maintenance on prediction in dynamic multi-objective optimization [J ] . Applied Soft Computing , 2017 , 58 : 631 - 647 .

郑金华 , 彭舟 , 邹娟 , 等 . 基于引导个体的预测策略求解动态多目标优化问题 [J ] . 电子学报 , 2015 , 43 ( 9 ): 1816 - 1825 .

ZHENG J H , PENG Z , ZOU J , et al . A prediction strategy based on guide-individual for dynamic multi-objective optimization [J ] . Acta Electronica Sinica , 2015 , 43 ( 9 ): 1816 - 1825 . (in Chinese)

马学敏 , 杨景明 , 孙浩 , 等 . 基于多区域中心点预测的动态多目标优化算法 [J ] . 控制与决策 , 2022 , 37 ( 10 ): 2477 - 2486 .

MA X M , YANG J M , SUN H , et al . Dynamic multi-objective optimization algorithm based on multi-regional center point prediction [J ] . Control and Decision , 2022 , 37 ( 10 ): 2477 - 2486 . (in Chinese)

JIANG M , WANG Z Z , HONG H K , et al . Knee point-based imbalanced transfer learning for dynamic multiobjective optimization [J ] . IEEE Transactions on Evolutionary Computation , 2021 , 25 ( 1 ): 117 - 129 .

李二超 , 周扬 . 基于分类的多策略预测方法求解动态多目标优化问题 [J ] . 控制与决策 , 2021 , 36 ( 7 ): 1569 - 1580 .

LI E C , ZHOU Y . Classification-based multi-strategy prediction method for dynamic multiobjective optimization problems [J ] . Control and Decision , 2021 , 36 ( 7 ): 1569 - 1580 . (in Chinese)

CHEN Y , ZOU J , LIU Y , et al . Combining a hybrid prediction strategy and a mutation strategy for dynamic multiobjective optimization [J ] . Swarm and Evolutionary Computation , 2022 , 70 : 101041 .

MURUGANANTHAM A , TAN K C , VADAKKEPAT P . Evolutionary dynamic multiobjective optimization via Kalman filter prediction [J ] . IEEE Transactions on Cybernetics , 2016 , 46 ( 12 ): 2862 - 2873 .

CHEN M , MA Y J . Dynamic multi-objective evolutionary algorithm with center point prediction strategy using ensemble Kalman filter [J ] . Soft Computing , 2021 , 25 ( 7 ): 5003 - 5019 .

胡振涛 , 杨诗博 , 胡玉梅 , 等 . 基于变分贝叶斯的分布式融合目标跟踪 [J ] . 电子学报 , 2022 , 50 ( 5 ): 1058 - 1065 .

HU Z T , YANG S B , HU Y M , et al . Distributed fusion target tracking based on variational Bayes [J ] . Acta Electronica Sinica , 2022 , 50 ( 5 ): 1058 - 1065 . (in Chinese)

WU Y , JIN Y C , LIU X X . A directed search strategy for evolutionary dynamic multiobjective optimization [J ] . Soft Computing , 2015 , 19 ( 11 ): 3221 - 3235 .

GOH C K , TAN K C . A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization [J ] . IEEE Transactions on Evolutionary Computation , 2009 , 13 ( 1 ): 103 - 127 .

JIANG S Y , YANG S X , YAO X , et al . Benchmark functions for the CEC'2018 competition on dynamic multiobjective optimization [R ] . Newcastle : Newcastle University , 2018 .

SIERRA M R , COELLO COELLO C A . Improving PSO-based multi-objective optimization using crowding, mutation and ∈-dominance [C ] // Evolutionary Multi-Criterion Optimization . Berlin, Heidelberg : Springer Berlin Heidelberg , 2005 : 505 - 519 .

WHILE L , HINGSTON P , BARONE L , et al . A faster algorithm for calculating hypervolume [J ] . IEEE Transactions on Evolutionary Computation , 2006 , 10 ( 1 ): 29 - 38 .

CAO L L , XU L H , GOODMAN E D , et al . Evolutionary dynamic multiobjective optimization assisted by a support vector regression predictor [J ] . IEEE Transactions on Evolutionary Computation , 2020 , 24 ( 2 ): 305 - 319 .

JIANG M , HUANG Z Q , QIU L M , et al . Transfer learning-based dynamic multiobjective optimization algorithms [J ] . IEEE Transactions on Evolutionary Computation , 2018 , 22 ( 4 ): 501 - 514 .

ZHANG Q F , LI H . MOEA/D: A multiobjective evolutionary algorithm based on decomposition [J ] . IEEE Transactions on Evolutionary Computation , 2007 , 11 ( 6 ): 712 - 731 .

DEB K , RAO N U B , KARTHIK S . Dynamic multi-objective optimization and decision-making using modified NSGA-II: A case study on hydro-thermal power scheduling [C ] // International Conference on Evolutionary Multi-Criterion Optimization . Berlin, Heidelberg : Springer Berlin Heidelberg , 2007 : 803 - 817 .

JIANG S Y , YANG S X . A steady-state and generational evolutionary algorithm for dynamic multiobjective optimization [J ] . IEEE Transactions on Evolutionary Computation , 2017 , 21 ( 1 ): 65 - 82 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于变分贝叶斯的分布式融合目标跟踪

基于引导个体的预测策略求解动态多目标优化问题

非线性系统故障诊断的粒子滤波方法

局域跟踪的测速雷达网量测融合弹道解算