Work

Exploring Diversity-Aware Augmented Learning for Multi-Solution Optimization

Year: 2025

Type: article

Abstract: Machine learning has proven highly effective in addressing constrained optimization problems by approximating the mapping from hyperparameters to solutions. However, standard supervised learning methods often fall short due to the presence of multiple (sub-)optimal solutions. To address this challenge, we propose a diversity-aware augmented learning framework. Our approach transforms the one-to-many input-solution mapping into a function through the augmentation of the input space with initial points, thereby respecting the diversity of high-quality solutions. The proposed framework enhances the quality and diversity of optimal solution estimation, as evidenced by two case studies.

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2025.07.051

Authors: Yanfang Mo, Xiang Pan, Zhongxi Zhu

Institutions: Lingnan University, Chinese University of Hong Kong

Language: English

Topic: Advanced Multi-Objective Optimization Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: Research Grants Council, University Grants Committee, Lingnan University, General Research Fund of Shanghai Normal University

Awards: G4017873074, G7173780561, G8185042842