Dynamic Multi-Objective Evolutionary Algorithm Based on Angle Correction and Hierarchical Multi-Population

YANG Le; MA Yong-jie; PING Hao-yu; YANG Yue

doi:10.12263/DZXB.20221330

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Dynamic Multi-Objective Evolutionary Algorithm Based on Angle Correction and Hierarchical Multi-Population

PAPERS | 更新时间：2025-12-08

- Dynamic Multi-Objective Evolutionary Algorithm Based on Angle Correction and Hierarchical Multi-Population
- ACTA ELECTRONICA SINICA Vol. 52, Issue 9, Pages: 3278-3290(2024)
- 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：
- 基金信息：
  
  National Natural Science Foundation of China(62066041)
- DOI：10.12263/DZXB.20221330
  CLC： TP18
- Received：21 November 2022，
  
  Revised：2023-07-17，
  
  Published：25 September 2024
- 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.

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references

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