Hyperbolic Neural Network Based Preselection for Expensive Multi-Objective Optimization

Bingdong Li1; Yanting Yang1; Wenjing Hong2; Peng Yang3,4; Aimin Zhou1

2024

10.1109/TEVC.2024.3409431

IEEE Transactions on Evolutionary Computation   影响因子和分区

1941-0026

A series of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed for expensive multi-objective optimization problems (EMOPs), building cheap surrogate models to replace the expensive real function evaluations. However, the search efficiency of these SAEAs is not yet satisfactory. More efforts are needed to further exploit useful information from the real function evaluations in order to better guide the search process. Facing this challenge, this paper proposes a Hyperbolic Neural Network (HNN) based preselection operator to accelerate the optimization process based on limited evaluated solutions. First, the preselection task is modeled as a multi-label classification problem where solutions are classified into different layers (ordinal categories) through -relaxed objective aggregation. Second, in order to resemble the hierarchical structure of candidate solutions, a hyperbolic neural network is applied to tackle the multi-label classification problem. The reason for using HNN is that hyperbolic spaces more closely resemble hierarchical structures than Euclidean spaces. Moreover, to alleviate the data deficiency issue, a data augmentation strategy is employed for training the HNN. In order to evaluate its performance, the proposed HNN-based preselection operator is embedded into two surrogate-assisted evolutionary algorithms. Experimental results on two benchmark test suites and three real-world problems with up to 11 objectives and 150 decision variables involving seven state-of-the-art algorithms demonstrate the effectiveness of the proposed method.

EI

其他

COMPUTER SCIENCE

1.Shanghai Institute of AI for Education, the School of Computer Science and Technology, and the Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, East China Normal University, Shanghai, China
2.National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China
3.Department of Computer Science and Engineering, Guangdong Key Laboratory of Brain-Inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, China
4.Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China

Bingdong Li,Yanting Yang,Wenjing Hong,et al. Hyperbolic Neural Network Based Preselection for Expensive Multi-Objective Optimization[J]. IEEE Transactions on Evolutionary Computation,2024,PP(99).

Bingdong Li,Yanting Yang,Wenjing Hong,Peng Yang,&Aimin Zhou.(2024).Hyperbolic Neural Network Based Preselection for Expensive Multi-Objective Optimization.IEEE Transactions on Evolutionary Computation,PP(99).

Bingdong Li,et al."Hyperbolic Neural Network Based Preselection for Expensive Multi-Objective Optimization".IEEE Transactions on Evolutionary Computation PP.99(2024).