42 citations · 55 across the 9 of their papers we have counts for
9 papers
Pointerformer: Deep Reinforced Multi-Pointer Transformer for the Traveling Salesman Problem
Yan Jin, Yuandong Ding, Xuanhao Pan +5
Traveling Salesman Problem (TSP), as a classic routing optimization problem originally arising in the domain of transportation and logistics, has become a critical task in broader…
H-TSP: Hierarchically Solving the Large-Scale Travelling Salesman Problem
Xuanhao Pan, Yan Jin, Yuandong Ding +4
We propose an end-to-end learning framework based on hierarchical reinforcement learning, called H-TSP, for addressing the large-scale Travelling Salesman Problem (TSP). The propos…
Two New Upper Bounds for the Maximum k-plex Problem
Jiongzhi Zheng, Mingming Jin, Kun He
A k-plex in a graph is a vertex set where each vertex is non-adjacent to at most k vertices (including itself) in this set, and the Maximum k-plex Problem (MKP) is to find the larg…
Incorporating Multi-armed Bandit with Local Search for MaxSAT
Jiongzhi Zheng, Kun He, Jianrong Zhou +3
Partial MaxSAT (PMS) and Weighted PMS (WPMS) are two practical generalizations of the MaxSAT problem. In this paper, we propose a local search algorithm for these problems, called…
Reinforced Lin-Kernighan-Helsgaun Algorithms for the Traveling Salesman Problems
Jiongzhi Zheng, Kun He, Jianrong Zhou +2
TSP is a classical NP-hard combinatorial optimization problem with many practical variants. LKH is one of the state-of-the-art local search algorithms for the TSP. LKH-3 is a power…
BandMaxSAT: A Local Search MaxSAT Solver with Multi-armed Bandit
Jiongzhi Zheng, Kun He, Jianrong Zhou +3
We address Partial MaxSAT (PMS) and Weighted PMS (WPMS), two practical generalizations of the MaxSAT problem, and propose a local search algorithm for these problems, called BandMa…
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman Problem
Jiongzhi Zheng, Kun He, Jianrong Zhou +2
We address the Traveling Salesman Problem (TSP), a famous NP-hard combinatorial optimization problem. And we propose a variable strategy reinforced approach, denoted as VSR-LKH, wh…
Stochastic Item Descent Method for Large Scale Equal Circle Packing Problem
Kun He, Min Zhang, Jianrong Zhou +2
Stochastic gradient descent (SGD) is a powerful method for large-scale optimization problems in the area of machine learning, especially for a finite-sum formulation with numerous…
A memetic algorithm for the minimum sum coloring problem
Yan Jin, Jin-Kao Hao, Jean-Philippe Hamiez
Given an undirected graph , the Minimum Sum Coloring problem (MSCP) is to find a legal assignment of colors (represented by natural numbers) to each vertex of such that the…