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In this work, we aim to explore connections between dynamical systems techniques and combinatorial optimization problems. In particular, we construct heuristic approaches for the traveling salesman problem (TSP) based on embedding the…

离散数学 · 计算机科学 2019-08-14 Tuhin Sahai , Adrian Ziessler , Stefan Klus , Michael Dellnitz

Combinatorial optimization serves as an essential part in many modern industrial applications. A great number of the problems are offline setting due to safety and/or cost issues. While simulation-based approaches appear difficult to…

机器学习 · 计算机科学 2020-07-21 Wenpeng Wei , Toshiko Aizono

The Travelling Salesman Problem (TSP) is a well known and challenging combinatorial optimisation problem. Its computational intractability has attracted a number of heuristic approaches to generate satisfactory, if not optimal, candidate…

新兴技术 · 计算机科学 2013-03-27 Jeff Jones , Andrew Adamatzky

In order to deal with the high development time of exact and approximation algorithms for NP-hard combinatorial optimisation problems and the high running time of exact solvers, deep learning techniques have been used in recent years as an…

机器学习 · 计算机科学 2021-04-20 James Fitzpatrick , Deepak Ajwani , Paula Carroll

The maximum traveling salesman problem (Max TSP) consists of finding a Hamiltonian cycle with the maximum total weight of the edges in a given complete weighted graph. This problem is APX-hard in the general metric case but admits…

数据结构与算法 · 计算机科学 2021-08-24 Vladimir Shenmaier

This work presents a tensor-network formulation of the Traveling Salesman Problem (TSP) and several of its variants. The approach represents candidate tours with tensor-network layers, weights them by Boltzmann factors, and enforces…

量子物理 · 物理学 2026-05-18 Alejandro Mata Ali , Iñigo Perez Delgado , Aitor Moreno Fdez. de Leceta

Routing problems are a class of combinatorial problems with many practical applications. Recently, end-to-end deep learning methods have been proposed to learn approximate solution heuristics for such problems. In contrast, classical…

机器学习 · 计算机科学 2021-12-06 Wouter Kool , Herke van Hoof , Joaquim Gromicho , Max Welling

With applications to many disciplines, the traveling salesman problem (TSP) is a classical computer science optimization problem with applications to industrial engineering, theoretical computer science, bioinformatics, and several other…

人工智能 · 计算机科学 2017-05-26 Yihui He , Ming Xiang

In this paper we schedule the travel path of a set of drones across a graph where the nodes need to be visited multiple times at pre-defined points in time. This is an extension of the well-known multiple traveling salesman problem. The…

人工智能 · 计算机科学 2021-01-06 Emmanouil Rigas , Panayiotis Kolios , Georgios Ellinas

The moving target traveling salesman problem with obstacles (MT-TSP-O) seeks an obstacle-free trajectory for an agent that intercepts a given set of moving targets, each within specified time windows, and returns to the agent's starting…

机器人学 · 计算机科学 2025-04-24 Anoop Bhat , Geordan Gutow , Bhaskar Vundurthy , Zhongqiang Ren , Sivakumar Rathinam , Howie Choset

The traveling salesman problem (TSP) is one of the most prominent combinatorial optimization problems. Given a complete graph G = (V, E) and non-negative distances d for every edge, the TSP asks for a shortest tour through all vertices with…

最优化与控制 · 数学 2021-09-30 Ulrich Pferschy , Rostislav Stanek

This paper addresses the Traveling Salesman Problem with Drone (TSP-D), in which a truck and drone are used to deliver parcels to customers. The objective of this problem is to either minimize the total operational cost (min-cost TSP-D) or…

人工智能 · 计算机科学 2019-11-20 Quang Minh Ha , Yves Deville , Quang Dung Pham , Minh Hoàng Hà

In this paper, we present a polynomial-sized linear programming formulation of the Traveling Salesman Problem (TSP). The proposed linear program is a network flow-based model. Numerical implementation issues and results are discussed. (The…

计算复杂性 · 计算机科学 2014-07-11 Moustapha Diaby

In this work we introduce an evolutionary strategy to solve combinatorial optimization tasks, i.e. problems characterized by a discrete search space. In particular, we focus on the Traveling Salesman Problem (TSP), i.e. a famous problem…

无序系统与神经网络 · 物理学 2016-08-05 Marco Alberto Javarone

This paper reviews the current progress in applying machine learning (ML) tools to solve NP-hard combinatorial optimization problems, with a focus on routing problems such as the traveling salesman problem (TSP) and the vehicle routing…

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 proposed H-TSP constructs a solution of a TSP instance starting…

人工智能 · 计算机科学 2023-04-20 Xuanhao Pan , Yan Jin , Yuandong Ding , Mingxiao Feng , Li Zhao , Lei Song , Jiang Bian

We study the Joint Routing-Assignment (JRA) problem in which items must be assigned one-to-one to placeholders while simultaneously determining a Hamiltonian cycle visiting all nodes exactly once. Extending previous exact MIP solvers with…

人工智能 · 计算机科学 2025-12-23 Yuan Qilong , Michal Pavelka

Given a set $P$ of $n$ points with their pairwise distances, the traveling salesman problem (TSP) asks for a shortest tour that visits each point exactly once. A TSP instance is rectilinear when the points lie in the plane and the distance…

数据结构与算法 · 计算机科学 2019-07-24 Hadrien Cambazard , Nicolas Catusse

The Traveling-Salesperson-Problem (TSP) is arguably one of the best-known NP-hard combinatorial optimization problems. The two sophisticated heuristic solvers LKH and EAX and respective (restart) variants manage to calculate close-to…

人工智能 · 计算机科学 2020-05-28 Jakob Bossek , Pascal Kerschke , Heike Trautmann

The Travelling Thief Problem (TTP) is a challenging combinatorial optimization problem that attracts many scholars. The TTP interconnects two well-known NP-hard problems: the Travelling Salesman Problem (TSP) and the 0-1 Knapsack Problem…

人工智能 · 计算机科学 2020-12-17 Lei Yang , Zitong Zhang , Xiaotian Jia , Peipei Kang , Wensheng Zhang , Dongya Wang