Research on Reproduction Operator and Multi-objective Optimization Based on Multi-source Mating Selection Strategy

ZHANG Yi; LU Yi‐zhou; WANG Shuai; LU Tong‐tong

doi:10.12263/DZXB.20200397

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Research on Reproduction Operator and Multi-objective Optimization Based on Multi-source Mating Selection Strategy

PAPERS | 更新时间：2025-12-08

- Research on Reproduction Operator and Multi-objective Optimization Based on Multi-source Mating Selection Strategy
- ACTA ELECTRONICA SINICA Vol. 49, Issue 9, Pages: 1754-1760(2021)
- 作者机构：
  
  1.常州大学机械与轨道交通学院，江苏常州 213164
  2.常州大学商学院，江苏常州 213164
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20200397
  CLC： TP18
- Received：24 April 2020，
  
  Revised：2021-01-09，
  
  Published：25 September 2021
- 稿件说明：
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张屹,陆逸舟,王帅等.基于多源交配选择策略的重组算子与多目标优化研究[J].电子学报,2021,49(09):1754-1760.

ZHANG Yi,LU Yi‐zhou,WANG Shuai,et al.Research on Reproduction Operator and Multi-objective Optimization Based on Multi-source Mating Selection Strategy[J].ACTA ELECTRONICA SINICA,2021,49(09):1754-1760.
张屹,陆逸舟,王帅等.基于多源交配选择策略的重组算子与多目标优化研究[J].电子学报,2021,49(09):1754-1760. DOI： 10.12263/DZXB.20200397.

ZHANG Yi,LU Yi‐zhou,WANG Shuai,et al.Research on Reproduction Operator and Multi-objective Optimization Based on Multi-source Mating Selection Strategy[J].ACTA ELECTRONICA SINICA,2021,49(09):1754-1760. DOI： 10.12263/DZXB.20200397.

摘要

本文提出了一种基于多源交配选择的多目标进化算法（Multi-source Mating Selection based Multi-objective Evolutionary Algorithms

MMSEA）.在MMSEA算法中，谱聚类被用来挖掘种群规则特性，基于所获得的种群结构化信息设计了一种多源交配选择重组算子来引导算法搜索，通过为每个个体设置多个交配选择源，在利用相似个体重组加速算法收敛的同时较好地保持了种群的多样性.实验结果表明，所提重组算子可以有效提升算法性能，将MMSEA与多种主流的多目标进化算法进行实验对比研究与参数灵敏度分析的结果表明，MMSEA在求解具有复杂特性的典型多目标优化问题测试集时表现出较强的竞争力.

Abstract

This work proposes a multi-source mating selection based multi-objective evolutionary algorithm（MMSEA）. In MMSEA

the spectral clustering algorithm is used to exploit the property of the multi-objective optimization problems. Based on the obtained population structure information

a multi-source mating selection strategy is designed to guide the algorithm search. The convergence of the algorithm is accelerated and the diversity of the population is maintained by setting multiple mating selections for each individual and using similar-based reproduction. The experimental results show that the proposed reproduction operator can effectively improve the performance of the algorithm. MMSEA is experimentally compared with variety of mainstream multi-objective evolutionary algorithms

and parameter sensitivity is also performed. In these experiments

MMSEA demonstrates strong competitiveness over the other approaches in solving typical multi-objective optimization problems with complex characteristics.

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references

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