Towards Geometry-Aware Pareto Set Learning for Neural Multi-Objective Combinatorial Optimization

• URL: http://arxiv.org/abs/2405.08604v2
• Date: Fri, 24 May 2024 03:36:10 GMT
• Title: Towards Geometry-Aware Pareto Set Learning for Neural Multi-Objective Combinatorial Optimization
• Authors: Yongfan Lu, Zixiang Di, Bingdong Li, Shengcai Liu, Hong Qian, Peng Yang, Ke Tang, Aimin Zhou,
• Abstract summary: Multi-objective diversity optimization (MOCO) problems are prevalent in various real-world applications. Most existing neural MOCO methods rely on problem to transform an MOCO problem into a series of singe-objective diversity enhancement (SOCO) problems. These methods often approximate partial regions of the front because of ambiguous and time-consuming precise hypervolume calculation.
• Score: 19.631213689157995
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-objective combinatorial optimization (MOCO) problems are prevalent in various real-world applications. Most existing neural MOCO methods rely on problem decomposition to transform an MOCO problem into a series of singe-objective combinatorial optimization (SOCO) problems. However, these methods often approximate partial regions of the Pareto front and spend excessive time on diversity enhancement because of ambiguous decomposition and time-consuming precise hypervolume calculation. To address these limitations, we design a Geometry-Aware Pareto set Learning algorithm named GAPL, which provides a novel geometric perspective for neural MOCO via a Pareto attention model based on hypervolume expectation maximization. In addition, we propose a hypervolume residual update strategy to enable the Pareto attention model to capture both local and non-local information of the Pareto set/front. We also design a novel inference approach to further improve quality of the solution set and speed up hypervolume calculation. Experimental results on three classic MOCO problems demonstrate that our GAPL outperforms several state-of-the-art baselines via superior decomposition and efficient diversity enhancement.

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