基于SOM聚类和自适应算子选择的高维多目标进化算法

钟沛龙; 黎明; 何超; 陈昊

doi:10.12263/DZXB.20201438

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基于SOM聚类和自适应算子选择的高维多目标进化算法

学术论文 | 更新时间：2025-12-08

- 基于SOM聚类和自适应算子选择的高维多目标进化算法
- Many-Objective Evolutionary Algorithm Based on SOM Clustering and Adaptive Operator Selection
- 电子学报 2022年50卷第8期页码：1959-1974
- 作者机构：
  
  1.南昌航空大学信息工程学院，江西南昌 330063
  2.南昌航空大学无损检测技术教育部重点实验室，江西南昌 330063
  3.南京航空航天大学自动化学院，江苏南京 211106
- 作者简介：
  
  [ "钟沛龙男，1997年生，江西赣州人.南昌航空大学硕士研究生，主要研究方向为进化算法.E-mail: zplong33@163.com" ]
  [ "黎明（通讯作者）男，1965年生，江西樟树人.南昌航空大学信息工程学院教授，南京航空航天大学博士生导师.主要研究方向为图像处理与模式识别、智能计算等." ]
  [ "何超男，1992年生，浙江诸暨人.南京航空航天大学博士研究生.主要研究方向为进化算法与图像处理、模式识别等.E-mail: hechao92918@163.com" ]
  [ "陈昊男，1982年生，山东平度人.南昌航空大学信息工程学院教授.主要研究方向为进化算法理论与应用、图像处理与模式识别等.E-mail: chenhaoshl@163.com" ]
- 基金信息：
  
  国家自然科学基金(61772255;61866025;61866026;61961030);江西省教育厅科学技术项目(GJJ170608);江西省优势科技创新团队计划项目(20181BCB24008);江西省自然科学基金(20181BAB202025;20202BAB204036);无损检测教育部重点实验室开放基金(EW201708505);江西省研究生创新专项资金项目(YC2020-S520)
- DOI：10.12263/DZXB.20201438
  中图分类号： TP18;
- 收稿：2020-12-15，
  
  修回：2021-06-04，
  
  纸质出版：2022-08-25
- 稿件说明：
移动端阅览
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(08):1959-1974.

ZHONG Pei-long,LI Ming,HE Chao,et al.Many-Objective Evolutionary Algorithm Based on SOM Clustering and Adaptive Operator Selection[J].ACTA ELECTRONICA SINICA,2022,50(08):1959-1974.
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(08):1959-1974. DOI： 10.12263/DZXB.20201438.

ZHONG Pei-long,LI Ming,HE Chao,et al.Many-Objective Evolutionary Algorithm Based on SOM Clustering and Adaptive Operator Selection[J].ACTA ELECTRONICA SINICA,2022,50(08):1959-1974. DOI： 10.12263/DZXB.20201438.

摘要

在高维多目标进化算法中，通常利用重组算子产生优质子代来引导种群搜索，已有研究表明，利用相似个体进行重组可以提高子代个体质量.由于自组织映射（Self-Organizing Mapping，SOM）网络能够通过聚类的方式保持种群个体原有的拓扑逻辑关系并获得个体的相似信息，因此本文提出一种基于SOM聚类和自适应算子选择的高维多目标进化算法（Many-Objective Evolutionary Algorithm based on SOM Clustering and Adaptive Operator Selection，MaOEA-SCAOS）.本文首先通过自组织映射网络进行种群分类，提取个体数据结构信息，并利用相似性构建邻域交配池；然后根据类内个体支配信息进行自适应算子选择，提高算法搜索和收敛性能；最后，采用环境选择策略对种群进行多样性管理以保证种群在帕累托前沿均匀分布.仿真结果表明，本文提出的基于SOM聚类和自适应算子选择（SOM Clustering and Adaptive Operator Selection，SCAOS）方法在处理高维多目标优化问题时具有较强的竞争力并且性能指标整体优于其他方法.

Abstract

In the many-objective evolutionary algorithm

recombination operators are usually used to generate high-quality offspring to guide the population search. Previous studies have shown that using similar individuals to reorganize can improve the quality of individual offspring. Since the self-organizing maping(SOM) network can maintain the original topological relationship of the population individuals and obtain the similar information of the individuals through clustering

this paper proposes a many-objective evolutionary algorithm based on SOM clustering and adaptive operator selection(MaOEA-SCAOS). First

the proposed method use self-organizing mapping network to classify the population

extract individual data structure information

and use similarity to build a neighborhood mating pool. Then the method select the adaptive operator based on the individual dominance information in the class to improve the search and convergence performance. Finally

the environmental selection strategy is adopted to manage the diversity of the population to ensure that the population is evenly distributed in the Pareto front. The experimental simulation results show that the SOM clustering and adaptive operator selection(SCAOS) method proposed in this paper has strong competitiveness while dealing with many-objective optimization problems

and the overall performance index is better than other methods.

关键词

Keywords

references

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