Computer Science / Computational Geometry

Generating Diverse TSP Tours via a Combination of Graph Pointer Network and Dispersion

February 09, 2026

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📝 Original Info

Title: Generating Diverse TSP Tours via a Combination of Graph Pointer Network and Dispersion
ArXiv ID: 2601.01132
Date: 2026-01-03
Authors: Hao-Tsung Yang, Ssu-Yuan Lo, Kuan-Lun Chen, Ching-Kai Wang

📝 Abstract

We address the Diverse Traveling Salesman Problem (D-TSP), a bi-criteria optimization challenge that seeks a set of k distinct TSP tours. The objective requires every selected tour to have a length at most c|T * | (where |T * | is the optimal tour length) while minimizing the average Jaccard similarity across all tour pairs. This formulation is crucial for applications requiring both high solution quality and fault tolerance, such as logistics planning, robotics pathfinding or strategic patrolling. Current methods are limited: traditional heuristics, such as the Niching Memetic Algorithm (NMA) or bicriteria optimization, incur high computational complexity (O(n 3 )), while modern neural approaches (e.g., RF-MA3S) achieve limited diversity quality and rely on complex, external mechanisms (e.g., active search and relativization filters). To overcome these limitations, we propose a novel hybrid framework that decomposes D-TSP into two efficient steps. First, we utilize a simple Graph Pointer Network (GPN), augmented with an approximated sequence entropy loss, to efficiently sample a large, diverse pool of high-quality tours. This simple modification effectively controls the quality-diversity trade-off without c...

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Generating Diverse TSP Tours via a Combination of Graph Pointer Network and Dispersion

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