一种基于改进集成学习分类的代理辅助进化算法
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作者单位:

西安建筑科技大学管理学院,西安市智慧工业感知计算与决策重点实验室

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中图分类号:

TP273

基金项目:

国家自然科学基金项目(51774228,51974223,52074205),陕西省自然科学基金杰青项目(2020JC-44)


An Improved Ensemble Learning Classification Based Surrogate-assisted Evolutionary Algorithm
Author:
Affiliation:

School of management,Xi’an University of Architecture and Technology,Xi''an Key Laboratory of Intelligent Industry Perception Computing and Decision Making

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    当使用代理辅助进化算法求解昂贵高维多目标优化问题时,代理模型通常用于近似昂贵的适应度函数.然而随着目标数的增加,近似误差将逐渐累积,计算量也会急剧增加.为了解决这一难题,本文提出了一种基于改进集成学习分类的代理辅助进化算法,使用一种改进的装袋集成学习分类器作为代理模型,首先从被昂贵的适应度评价的个体中选择一组分类边界,将所有个体分成两类;其次利用这些带有分类标签的个体训练分类器,来对候选个体的类别进行预测;最后选择有前途的个体进行昂贵适应度评价.实验结果证明,算法中所提代理模型有效提高了基于分类的代理辅助进化算法求解昂贵高维多目标优化问题的能力,且与目前流行的代理辅助进化算法相比,基于改进集成学习分类的代理辅助进化算法更具竞争力.

    Abstract:

    When using surrogate-assisted evolutionary algorithm to solve the expensive many-objective optimization problems, the surrogate is usually used to approximate the expensive fitness function. However, with the increase of the number of objective, the approximation error will accumulate gradually and the amount of calculation will increase sharply. In order to solve this problem, we proposed an improved ensemble learning classification based surrogate-assisted evolutionary algorithm. This algorithm uses an improved bagging ensemble as the surrogate. Firstly, a set of classification boundary individuals are selected from the individuals evaluated by the expensive fitness function, the individuals are divided into two groups. Secondly, these individuals with the group labels are used to train a classifier to predict the groups of the candidate individuals. Finally, the promising individuals are selected to be evaluated by the expensive fitness function. The experimental results show that the proposed surrogate in the algorithm effectively improves the ability of the classification based surrogate-assisted evolutionary algorithm to solve the expensive many-objective optimization problems, and compared with the current popular surrogate-assisted evolutionary algorithms, the proposed improved ensemble learning classification based surrogate-assisted evolutionary algorithm is more competitive.

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  • 收稿日期:2021-03-08
  • 最后修改日期:2021-08-09
  • 录用日期:2021-07-05
  • 在线发布日期: 2021-08-10
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