Learnable Evolutionary Multi-Objective Combinatorial Optimization via Sequence-to-Sequence Model

TL;DR

SeqMO is a novel approach combining sequence-to-sequence models with evolutionary algorithms to improve multi-objective combinatorial optimization, effectively guiding solution search while maintaining diversity.

Contribution

It introduces a learnable framework that integrates sequence-to-sequence models with evolutionary algorithms for discrete multi-objective optimization.

Findings

Effective on TSP and quadratic assignment problems

Outperforms traditional evolutionary algorithms

Maintains solution diversity during search

Abstract

Recent advances in learnable evolutionary algorithms have demonstrated the importance of leveraging population distribution information and historical evolutionary trajectories. While significant progress has been made in continuous optimization domains, combinatorial optimization problems remain challenging due to their discrete nature and complex solution spaces. To address this gap, we propose SeqMO, a novel learnable multi-objective combinatorial optimization method that integrates sequence-to-sequence models with evolutionary algorithms. Our approach divides approximate Pareto solution sets based on their objective values' distance to the Pareto front, and establishes mapping relationships between solutions by minimizing objective vector angles in the target space. This mapping creates structured training data for pointer networks, which learns to predict promising solution trajectories in the discrete search space. The trained model then guides the evolutionary process by generating new candidate solutions while maintaining population diversity. Experiments on the multi-objective travel salesman problem and the multi-objective quadratic assignment problem verify the effectiveness of the algorithm. Our code is available at: \href{https://github.com/jiaxianghuang/SeqMO}{https://github.com/jiaxianghuang/SeqMO}.