Constrained Dropout Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Problems

ZHANG Rui; BAI Xiao-lu; PAN Li-hu

doi:10.12263/DZXB.20220694

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Constrained Dropout Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Problems

PAPERS | 更新时间：2025-12-08

- Constrained Dropout Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Problems
- ACTA ELECTRONICA SINICA Vol. 51, Issue 7, Pages: 1859-1867(2023)
- 作者机构：
  
  太原科技大学计算机科学与技术学院，山西太原 030024
- 作者简介：
- 基金信息：
  
  Fundamental Research Program of Shanxi Province(20210302123216);Shanxi Province Engineering Research Center for Equipment Digitization and PHM(ZBPHM20201104);Postgraduate Education Reform Research Project of Shanxi Province(2021YJJG244);Postgraduate Education Innovation Project of Shanxi Province(2021Y699)
- DOI：10.12263/DZXB.20220694
  CLC： TP273
- Received：16 June 2022，
  
  Revised：2023-03-01，
  
  Published：25 July 2023
- 稿件说明：
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张睿,白晓露,潘理虎.求解高维昂贵多目标问题的约束型Dropout代理辅助进化算法[J].电子学报,2023,51(07):1859-1867.

ZHANG Rui,BAI Xiao-lu,PAN Li-hu.Constrained Dropout Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Problems[J].ACTA ELECTRONICA SINICA,2023,51(07):1859-1867.
张睿,白晓露,潘理虎.求解高维昂贵多目标问题的约束型Dropout代理辅助进化算法[J].电子学报,2023,51(07):1859-1867. DOI： 10.12263/DZXB.20220694.

ZHANG Rui,BAI Xiao-lu,PAN Li-hu.Constrained Dropout Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Problems[J].ACTA ELECTRONICA SINICA,2023,51(07):1859-1867. DOI： 10.12263/DZXB.20220694.

摘要

现实优化问题常需同时对多个冲突目标进行评估优化，由于评估过程多依赖大量复杂的仿真实验，从而产生计算代价昂贵的问题.代理模型辅助下的进化算法可在计算资源有限的情况下为此类问题提供有效的最优解集.但随着问题决策以及目标空间维度变高，则会衍生出诸多负面因素限制代理模型的预测精度. 对此，提出一种基于约束型Dropout神经网络的代理辅助进化算法（Constrained Dropout Neural Network based surrogate-assisted Evolution Algorithm，CDNNEA），以约束型Dropout神经网络作为一种可扩展方案来增强代理模型在高维空间中的适用性，在模型管理部分中，构建种群个体收敛性以及多样性评判指标，自适应地选取引导代理模型更新的代表性个体.通过在DTLZ基准测试问题上进行实验，CDNNEA显示出相较其它先进算法性能表现最优或近似最优，同时将其应用于高维参数自适应优化的现实问题中.实验表明：提供的代表性解决方案投入实际应用时识别准确率均较优且模型计算量可控，验证出CDNNEA在高维昂贵类优化问题上的有效性.

Abstract

Realistic optimization problems often need to evaluate and optimize multiple conflicting targets at the same time. Because the evaluation process depends on a large number of complex simulation experiments

the calculation cost is expensive. The evolutionary algorithms assisted by the surrogate models can provide effective optimal solution sets for such problems with limited computational resources. However

as the dimensions of decision space and target space become higher

many negative factors will be derived to limit the prediction accuracy of the surrogate model. Therefore

a surrogate-assisted evolution algorithm based on the constrained dropout neural network (CDNNEA) is proposed. The constrained dropout neural network is used as a scalable scheme to enhance the applicability of surrogate models in high-dimensional space. In the model management part

the convergence and diversity evaluation indicators of the population individuals are constructed

in order to adaptively select the representative individuals to guide the surrogate model to update. Experiments on DTLZ benchmark problems show that CDNNEA performs the best or approximates the best compared with other advanced algorithms. Meanwhile

CDNNEA is applied to the high-dimensional parameter adaptive optimization of practical problems. The experimental results show the proposed representative solutions have better recognition accuracy and controllable model computation in practical application

which verifies the effectiveness of CDNNEA in high-dimensional expensive optimization problems.

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references

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