Data-Driven Preference Sampling for Pareto Front Learning

Rongguang Ye1; Lei Chen2; Weiduo Liao1; Jinyuan Zhang1; Hisao Ishibuchi1

10.1109/IJCNN60899.2024.10651267

2024-07-05

2161-4393

979-8-3503-5932-9

2024 International Joint Conference on Neural Networks (IJCNN)

30 June-5 July 2024

Yokohama, Japan

Pareto front learning is a technique that introduces preference vectors in a neural network to approximate the Pareto front. Previous Pareto front learning methods have demonstrated high performance in approximating simple Pareto fronts. These methods often sample preference vectors from a fixed Dirichlet distribution. However, no fixed sampling distribution can be adapted to diverse Pareto fronts. Efficiently sampling preference vectors and accurately estimating the Pareto front is a challenge. To address this challenge, we propose a data-driven preference vector sampling framework for Pareto front learning. We utilize the posterior information of the objective functions to adjust the parameters of the sampling distribution flexibly. In this manner, the proposed method can sample preference vectors from the location of the Pareto front with a high probability. Moreover, we design the distribution of the preference vector as a mixture of Dirichlet distributions to improve the performance of the model in disconnected Pareto fronts. Extensive experiments validate the superiority of the proposed method compared with state-of-the-art algorithms.

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1.Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, China
2.School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou, China

Rongguang Ye,Lei Chen,Weiduo Liao,et al. Data-Driven Preference Sampling for Pareto Front Learning[C],2024.