A Large-Scale Constrained Multi-Objective Optimization Algorithm Based on Solution Space Reduction

WANG Chao; HUANG Hui-tao; ZHANG Jing; QIU Jian-feng

doi:10.12263/DZXB.20230548

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A Large-Scale Constrained Multi-Objective Optimization Algorithm Based on Solution Space Reduction

PAPERS | 更新时间：2025-12-08

- A Large-Scale Constrained Multi-Objective Optimization Algorithm Based on Solution Space Reduction
- ACTA ELECTRONICA SINICA Vol. 51, Issue 11, Pages: 3120-3127(2023)
- 作者机构：
  
  1.安徽大学信息材料与智能感知安徽省实验室,安徽合肥 230601
  2.安徽大学人工智能学院,安徽合肥 230601
  3.安徽大学自主无人系统技术教育部工程研究中心,安徽合肥 230601
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62106002);Natural Science Foundation of Anhui Province(2008085QF308;2308085MF201)
- DOI：10.12263/DZXB.20230548
  CLC： TP18
- Received：13 June 2023，
  
  Revised：2023-08-04，
  
  Published：25 November 2023
- 稿件说明：
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王朝,黄慧涛,张晶等.基于解空间降维的大规模约束多目标进化算法[J].电子学报,2023,51(11):3120-3127.

WANG Chao,HUANG Hui-tao,ZHANG Jing,et al.A Large-Scale Constrained Multi-Objective Optimization Algorithm Based on Solution Space Reduction[J].ACTA ELECTRONICA SINICA,2023,51(11):3120-3127.
王朝,黄慧涛,张晶等.基于解空间降维的大规模约束多目标进化算法[J].电子学报,2023,51(11):3120-3127. DOI： 10.12263/DZXB.20230548.

WANG Chao,HUANG Hui-tao,ZHANG Jing,et al.A Large-Scale Constrained Multi-Objective Optimization Algorithm Based on Solution Space Reduction[J].ACTA ELECTRONICA SINICA,2023,51(11):3120-3127. DOI： 10.12263/DZXB.20230548.

摘要

针对大规模约束多目标优化问题呈现的高维度和约束限制的解空间，提出一种基于自编码器的解空间降维方法，用以提升进化算法搜索效率.首先，设计一种可行性标签配对策略训练自编码器，通过同时利用解的可行与不可行两类标签信息，构建包含可行域拓扑信息的降维子空间；其次，在降维后的子空间中进行遗传操作，通过解码器得到重构输出返回原始空间，快速定位潜在的可行区域；最后，设计一种子代自适应生成策略，通过结合在降维空间和原始空间生成的子代优势，防止模型坍塌同时提高搜索效率.在基准测试问题集上与五种先进算法进行对比，实验结果表明所提方法能获得更快的收敛速度和更好的解集质量.

Abstract

To tackle the challenges posed by high-dimensional and constrained solution spaces in large-scale constrained multi-objective optimization problems

this study employs an autoencoder-based solution space reduction technique to enhance the search efficiency of evolutionary algorithms. Firstly

a feasibility label pairing strategy is designed to train the autoencoder. By incorporating both feasible and infeasible solutions as two distinct classes of samples

a subspace can be constructed that captures the topological information of feasible regions. Also

this subspace can be regarded as the reduced representation of the original solution space. Secondly

the genetic operator is applied within the reduced subspace to produce the offspring

and the reconstructed outputs are subsequently mapped back to the original solution space using the decoder. This process can enable the location of the potential feasible regions. Lastly

an adaptive generation strategy is introduced to combine the advantages of offspring generated within both the reduced subspace and the original space

to prevent the model collapse and enhance the search efficiency. To validate the performance of the proposed algorithm

a comparative analysis is conducted against five state-of-the-art algorithms using publicly available test suites. The experimental results demonstrate that the proposed algorithm exhibits faster convergence speed and produces solutions of superior quality.

关键词

Keywords

references

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