Fuzzy classification pre-selection based surrogate-assisted multi-objective evolutionary algorithm

被引：0

作者：

Li, Er-Chao ^{[1
]}

Wu, Yu ^{[1
]}

机构：

[1] College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou

来源：

Kongzhi yu Juece/Control and Decision | 2025年 / 40卷 / 02期

关键词：

classification preselection; classification surrogate model; evolutionary algorithm; expensive many-objective optimization; model management; surrogate-assisted evolutionary algorithm;

D O I：

10.13195/j.kzyjc.2024.0103

中图分类号：

学科分类号：

摘要：

The demand for solving expensive high-dimensional multi-objective optimization problems is gradually increasing. When traditional regression models are used to tackle such issues, cumulative error and computational complexity tend to surge significantly. To enhance the search efficiency of agent-assisted evolutionary algorithms and strike a balance between convergence and diversity in high-dimensional multi-objective problems, this paper introduces a fuzzy classification pre-selection based surrogate-assisted multi-objective evolutionary algorithm (FCPSEA). Firstly, the population is initialized and evaluated, and two training sample sets are constructed using non-dominated relationships and congestion degree. Then, the training samples and a double-archive operator guide the classifier to categorize more accurately. Finally, a model management strategy is proposed based on fuzzy classification pre-selection, which is set according to the predicted double-archive class labels and membership degrees. To validate the performance of the proposed algorithm, comparative experiments are conducted with classical algorithms in recent years on two groups of test problems encompassing various features. The experimental results demonstrate that the algorithm exhibits strong competitiveness in solving expensive high-dimensional multi-objective optimization problems. © 2025 Northeast University. All rights reserved.

引用

页码：553 / 562

页数：9

共 24 条

[1]

Zheng J H., Multi-objective evolutionary algorithm and its application, pp. 1-55, (2007)

[2]

Xu S S, Li J H, Li L, Et al., Decomposition and cluster based expensive many-objective evolutionary algorithm, Control and Decision, 39, 2, pp. 440-448, (2024)

[3]

Wang H, Sun C L, Zhang G C., Sampling based on mean value of ranking on approximation uncertainties for expensive many-objective evolutionary algorithm, Control and Decision, 38, 12, pp. 3317-3326, (2023)

[4]

Guo S., Research on intelligent optimization method and application of complex industrial process driven by data and model, (2019)

[5]

Li Y J., The application analysis of artificial intelligence in computer network technology, 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers, pp. 1126-1129, (2021)

[6]

Gu Q H, Zhang X Y, Chen L., Improved ensemble learning classification based surrogate-assisted evolutionary algorithm, Control and Decision, 37, 10, pp. 2456-2466, (2022)

[7]

Song Z S, Wang H D, He C, Et al., A kriging-assisted two-archive evolutionary algorithm for expensive many-objective optimization, IEEE Transactions on Evolutionary Computation, 25, 6, pp. 1013-1027, (2021)

[8]

Pan L Q, He C, Tian Y, Et al., A classification-based surrogate-assisted evolutionary algorithm for expensive many-objective optimization, IEEE Transactions on Evolutionary Computation, 23, 1, pp. 74-88, (2019)

[9]

Zhang J Y, Zhou A M, Zhang G X., A classification and Pareto domination based multiobjective evolutionary algorithm, 2015 IEEE Congress on Evolutionary Computation, pp. 2883-2890, (2015)

[10]

Hao H, Zhou A M, Qian H, Et al., Expensive multiobjective optimization by relation learning and prediction, IEEE Transactions on Evolutionary Computation, 26, 5, pp. 1157-1170, (2022)

← 1 2 3 →