Research on Recombination Operator and Multi-Objective Optimization Based on Fitness Guided Mating Restriction Strategy

WANG Yu-hong; ZHANG Yi

doi:10.12263/DZXB.20210048

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Research on Recombination Operator and Multi-Objective Optimization Based on Fitness Guided Mating Restriction Strategy

PAPERS | 更新时间：2025-12-11

- Research on Recombination Operator and Multi-Objective Optimization Based on Fitness Guided Mating Restriction Strategy
- ACTA ELECTRONICA SINICA Vol. 50, Issue 3, Pages: 710-717(2022)
- 作者机构：
  
  常州大学机械与轨道交通学院，江苏常州 213164
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20210048
  CLC： TP18
- Received：31 December 2020，
  
  Revised：2021-08-13，
  
  Published：25 March 2022
- 稿件说明：
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汪禹宏,张屹.基于适应度指导交配限制策略的重组算子与多目标优化研究[J].电子学报,2022,50(03):710-717.

WANG Yu-hong,ZHANG Yi.Research on Recombination Operator and Multi-Objective Optimization Based on Fitness Guided Mating Restriction Strategy[J].ACTA ELECTRONICA SINICA,2022,50(03):710-717.
汪禹宏,张屹.基于适应度指导交配限制策略的重组算子与多目标优化研究[J].电子学报,2022,50(03):710-717. DOI： 10.12263/DZXB.20210048.

WANG Yu-hong,ZHANG Yi.Research on Recombination Operator and Multi-Objective Optimization Based on Fitness Guided Mating Restriction Strategy[J].ACTA ELECTRONICA SINICA,2022,50(03):710-717. DOI： 10.12263/DZXB.20210048.

摘要

本文提出了一种基于K-means聚类适应度指导交配限制的多目标优化算法（K-means clustering-based Fitness Guided mating restriction multi-objective Evolutionary Algorithm，KFGEA）.在该算法的迭代过程中，利用K-means聚类算法从全局角度提取种群结构化信息.基于聚类所得的全局信息，本文围绕个体局部信息设计了一种适应度指导交配限制策略去完成全局与局部信息的融合.该策略根据适应度值这一确定性信息来判断个体质量，对非支配解进行近邻重组，对支配解进行全局勘探，去维护算法搜索过程中开采和勘探的平衡.将KFGEA与多种主流的多目标进化算法进行试验对比研究与参数灵敏度分析的结果表明，KFGEA在求解具有复杂特性的多目标优化问题时优势明显，该策略可以有效提高多目标进化算法的性能.

Abstract

This paper proposes a multi-objective evolutionary algorithm with clustering based fitness guided mating restriction strategy (KFGEA). In the iteration process of this algorithm

K-means clustering algorithm is used to extract population structure information from a global perspective.Based on the global information obtained by clustering

this paper designs a fitness-guided mating restriction strategy around individual local information to complete the fusion of global and local information.This strategy judges the individual quality based on the deterministic information of fitness value

recombines the non-dominant solution with its neighbors

explores the dominant solution globally

and maintains the balance between exploitation and exploration during the algorithm search process.Compared with other mainstream multi-objective evolutionary algorithms

KFGEA has obvious advantages in solving multi-objective optimization problems with complex characteristics. The results show that this strategy can effectively improve the performance of multi-objective evolutionary algorithm.

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references

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