电子学报2023，Vol.51Issue(11)：3120-3127,8.DOI:10.12263/DZXB.20230548
基于解空间降维的大规模约束多目标进化算法
A Large-Scale Constrained Multi-Objective Optimization Algorithm Based on Solution Space Reduction
摘要
Abstract
To tackle the challenges posed by high-dimensional and constrained solution spaces in large-scale con-strained 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 re-duced 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 experi-mental results demonstrate that the proposed algorithm exhibits faster convergence speed and produces solutions of superior quality.关键词
大规模约束多目标优化/进化算法/自编码器/空间降维/子代生成/可行性Key words
large-scale constrained multi-objective optimization/evolutionary algorithms/auto-encoder/solution space reduction/offspring generation/feasibility分类
信息技术与安全科学引用本文复制引用
王朝,黄慧涛,张晶,邱剑锋..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):3120-3127,8.基金项目
国家自然科学基金(No.62106002) （No.62106002）
安徽省自然科学基金(No.2008085QF308,No.2308085MF201)National Natural Science Foundation of China(No.62106002) （No.2008085QF308,No.2308085MF201）
Natural Science Foundation of Anhui Province(No.2008085QF308,No.2308085MF201) （No.2008085QF308,No.2308085MF201）
