709 Hits in 6.3 sec

Clustering process to solve euclidean TSP

Abdulah Fajar, Nur Azman Abu, Nanna Suryana Herman, Shahrin Shahib
2010 2010 3rd International Conference on Computer Science and Information Technology  
There has been growing interest in studying combinatorial optimization problems by clustering approach, with a special emphasis on the Euclidean Traveling Salesman Problem.  ...  Human is able to cluster and filter object efficiently. Clustering problem has been approached from diverse domains of knowledge like graph theory, statistics, artificial neural network and so on.  ...  CLUSTERING APPROACH TO EUCLIDEAN TSP The first step coming from clustering process to generate small compact clusters as shown in Figure 7 . Each cluster shall be connected to adjacent clusters.  ... 
doi:10.1109/iccsit.2010.5563971 fatcat:5z6ww4klsbabhh6dzhl2aduycq

Learning to Optimise General TSP Instances [article]

Nasrin Sultana, Jeffrey Chan, A. K. Qin, Tabinda Sarwar
2020 arXiv   pre-print
We name this approach the non-Euclidean TSP network (NETSP-Net).  ...  Hence this paper introduces a new learning-based approach to solve a variety of different and common TSP problems that are trained on easier instances which are faster to train and are easier to obtain  ...  Acknowledgements The authors wish to thank Kendall Taylor for his valuable comments and helpful suggestions for figures which greatly improved the paper's quality.  ... 
arXiv:2010.12214v2 fatcat:mcsrqb4vqvhkpn76zwg4bnrb4q

Statistical mechanics of multi-index matching problems with site disorder

David S. Dean, David Lancaster
2006 Physical Review E  
We study the statistical mechanics of multi-index matching problems where the quenched disorder is a geometric site disorder rather than a link disorder.  ...  heuristics leading to that optimal match  ...  Acknowledgement: DSD would like to thank the Isaac Newton Insitute, Cambridge University, where part of this work was carried out during the program Principles of the Dynamics of Non-Equilibrium Systems  ... 
doi:10.1103/physreve.74.041122 pmid:17155037 fatcat:45kgxkymd5d43egy6nyvs2u4ki

On Randomness and Structure in Euclidean TSP Instances: a Study with Heuristic Methods

Gloria Cerasela Crisan, Elena Nechita, Dana Simian
2021 IEEE Access  
Prediction of the quality of the result provided by a specific solving method is an important factor when choosing how to solve a given problem.  ...  On small random instances, the ACO implementation used in this paper also optimally found the solutions.  ...  MAX-MIN Ant System (MMAS) comes with three modifications when compared to AS: • The pheromone is forced to belong to a specific interval [τ min , τ max ].  ... 
doi:10.1109/access.2020.3048774 fatcat:bs6cb5djsfgxnke7bwyb3urmta

Towards Feature-free TSP Solver Selection: A Deep Learning Approach [article]

Kangfei Zhao, Shengcai Liu, Yu Rong, Jeffrey Xu Yu
2021 arXiv   pre-print
To solve TSP efficiently, in addition to developing new TSP solvers, it needs to find a per-instance solver for each TSP instance, which is known as the TSP solver selection problem.  ...  In a large scale location-based services system, users issue TSP queries concurrently, where a TSP query is a TSP instance with n points.  ...  The raw coordinates in a TSP instance are re-scaled to an interval (0, 1) by min-max normalization.  ... 
arXiv:2006.00715v2 fatcat:iogwc4lc4bgebbfh4w5zz3t4aa

Scaling ant colony optimization with hierarchical reinforcement learning partitioning

Erik J. Dries, Gilbert L. Peterson
2008 Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08  
This research then transfers HRL techniques to the ACO domain and traveling salesman problem (TSP). To apply HRL to ACO, a hierarchy must be created for the TSP.  ...  This research also provides two specific implementations of the new algorithm: the first a modification to Dietterich's MAXQ-Q HRL algorithm, the second a hierarchical ACO algorithm.  ...  Tour Length Statistics for ACS-TSP (TSPLIB) . . . . . . . . . 48 5.8. Tour Length Statistics for ACS-TSP (Random) . . . . . . . . . 49 5.9.  ... 
doi:10.1145/1389095.1389100 dblp:conf/gecco/DriesP08 fatcat:7zahsvrg4fbajfrav3mwza5ryy

Leveraging TSP Solver Complementarity through Machine Learning

Pascal Kerschke, Lars Kotthoff, Jakob Bossek, Holger H. Hoos, Heike Trautmann
2017 Evolutionary Computation  
We leverage this complementarity to build an algorithm selector, which selects the best TSP solver on a perinstance basis and thus achieves significantly improved performance compared to the single best  ...  solver, representing an advance in the state of the art in solving the Euclidean TSP.  ...  Lars Kotthoff was supported by an NSERC Discovery Grant to Holger Hoos.  ... 
doi:10.1162/evco_a_00215 pmid:28836836 fatcat:5awgbc3sfngohgr6gq243v3vlm

Learning TSP Requires Rethinking Generalization [article]

Chaitanya K. Joshi, Quentin Cappart, Louis-Martin Rousseau, Thomas Laurent
2021 arXiv   pre-print
While state-of-the-art Machine Learning approaches perform closely to classical solvers when trained on trivially small sizes, they are unable to generalize the learnt policy to larger instances of practical  ...  Towards leveraging transfer learning to solve large-scale TSPs, this paper identifies inductive biases, model architectures and learning algorithms that promote generalization to instances larger than  ...  Acknowledgments We would like to thank X. Bresson, V. Dwivedi, A. Ferber, E. Khalil, W. Kool, R. Levie, A. Prouvost, P. Veličković and the anonymous reviewers for helpful comments and discussions.  ... 
arXiv:2006.07054v3 fatcat:fsxtrv2tzveabftlxzjuzpdura

On Scaling Multi-Agent Task Reallocation Using Market-Based Approach

Rajesh K. Karmani, Timo Latvala, Gul Agha
2007 First International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2007)  
We propose a novel market-based approach, called Market-based Approach with Look-ahead Agents (MALA), to address the problem. In MALA, agents use look ahead to optimize their behavior.  ...  We use simulations in a two dimensional world to study the performance of MALA and compare it with O-contracts and TraderBots, respectively, a centralized approach and a distributed approach.  ...  Acknowledgements We would like to thank Chandra Chekuri for suggesting the use of the Universal TSP algorithm and for providing useful insights into alternative approaches for planning tours.  ... 
doi:10.1109/saso.2007.41 dblp:conf/saso/KarmaniLA07 fatcat:jlvwzz4q6vg75f5t6sob733xai

An Improved Ant Colony Optimization Based on an Adaptive Heuristic Factor for the Traveling Salesman Problem

Pengzhen Du, Ning Liu, Haofeng Zhang, Jianfeng Lu, Jose E. Naranjo
2021 Journal of Advanced Transportation  
A modified 2-opt local optimizer is proposed to further tune the solution. Finally, a mechanism to jump out of the local optimum is introduced to avoid the stagnation of the algorithm.  ...  The traveling salesman problem (TSP) is a typical combinatorial optimization problem, which is often applied to sensor placement, path planning, etc.  ...  In [17] , an improved ABC algorithm based a novel neighborhood selection mechanism was proposed to solve the TSP problem, which enhanced the solution quality.  ... 
doi:10.1155/2021/6642009 fatcat:ij2sbatnbbabddj3o3nqudczxm

A Graph Neural Network Assisted Monte Carlo Tree Search Approach to Traveling Salesman Problem

Zhihao Xing, Shikui Tu
2020 IEEE Access  
Without much heuristic designing, our approach outperforms recent state-of-the-art learning-based methods on the TSP.  ...  We adopt a greedy algorithm framework to derive a promising tour by adding the vertices successively.  ...  ) according to the statistics in the search tree as given by (7) and (8) , respectively, where c puct is a constant to trading off between exploration and exploitation. a t = arg max a (Q(s t , a) +  ... 
doi:10.1109/access.2020.3000236 fatcat:dpholycgrnejff2732kkcxkmha

Monte Carlo Tree Search: Long-term versus short-term planning

Diego Perez, Philipp Rohlfshagen, Simon M. Lucas
2012 2012 IEEE Conference on Computational Intelligence and Games (CIG)  
Euclidean distance.  ...  in order to visit a series of waypoints as quickly as possible.  ...  TSP approach.  ... 
doi:10.1109/cig.2012.6374159 dblp:conf/cig/PerezRL12 fatcat:w7argafjofd3lncrlhpphxfrda

Comparison between Single and Multi-Objective Evolutionary Algorithms to Solve the Knapsack Problem and the Travelling Salesman Problem

Mohammed Mahrach, Gara Miranda, Coromoto León, Eduardo Segredo
2020 Mathematics  
Some common approaches to this topic are based on adding extra—and generally artificial—objectives to the problem formulation.  ...  One of the main components of most modern Multi-Objective Evolutionary Algorithms (MOEAs) is to maintain a proper diversity within a population in order to avoid the premature convergence problem.  ...  First, in the Euclidean instances, the costs between edges correspond to the Euclidean distance between two points on a plane, randomly sampled from a uniform distribution.  ... 
doi:10.3390/math8112018 fatcat:giikia23cjd6lev27jwrozyoem

A Hybrid Framework Using a QUBO Solver For Permutation-Based Combinatorial Optimization [article]

Siong Thye Goh, Sabrish Gopalakrishnan, Jianyuan Bo, Hoong Chuin Lau
2021 arXiv   pre-print
We tested our approach on provably hard Euclidean Traveling Salesman (E-TSP) instances and Flow Shop Problem (FSP).  ...  We propose a machine learning approach to tune the parameters for good performance effectively. To handle possible infeasibility, we introduce a polynomial-time projection algorithm.  ...  QUBO is closely related to the Ising model in Statistical Mechanics.  ... 
arXiv:2009.12767v2 fatcat:th6ai3cyyncuferoymuesf4g4i

Statistical mechanics methods and phase transitions in optimization problems

Olivier C. Martin, Rémi Monasson, Riccardo Zecchina
2001 Theoretical Computer Science  
We first introduce some elementary methods of statistical mechanics and then progressively cover the tools appropriate for disordered systems.  ...  We cover in detail the Random Graph, the Satisfiability, and the Traveling Salesman problems. References to the physics literature on optimization are provided.  ...  mechanics approach.  ... 
doi:10.1016/s0304-3975(01)00149-9 fatcat:7kn4ndpcfbf4jay7du4ntvnfaq
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