Solving Traveling Salesman Problem by Ant Colony Optimization Algorithm with Association Rule | IEEE Conference Publication | IEEE Xplore

Solving Traveling Salesman Problem by Ant Colony Optimization Algorithm with Association Rule


Abstract:

The traveling salesman problem (TSP) is among the most important combinatorial problems. Ant colony optimization (ACO) algorithm is a recently developed algorithm which h...Show More

Abstract:

The traveling salesman problem (TSP) is among the most important combinatorial problems. Ant colony optimization (ACO) algorithm is a recently developed algorithm which has been successfully applied to several NP-hard problems, such as traveling salesman problem, quadratic assignment problem and job-shop problem. Association rule (AR) is the key in knowledge in data mining for finding the best data sequence. A new algorithm which integrates ACO and AR is proposed to solve TSP problems. Compare with the simulated annealing algorithm, the standard genetic algorithm and the standard ant colony algorithm, the new algorithm is better than ACO.
Date of Conference: 24-27 August 2007
Date Added to IEEE Xplore: 05 November 2007
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Conference Location: Haikou, China
Citations are not available for this document.

1. Introduction

Recently Traveling Salesman Problem (TSP) is the importance research core for combinatorial problems in the world. TSP is proven to be NP-hard. Research methods applied to TSP has been extended to some fine heuristics such as simulated annealing algorithm [1], tabu search algorithm [2] [3], genetic algorithms[4], ant colony algorithm [5] [6] etc. In this paper we present a new method to efficiently solve TSP problems. We used association rules to assist ant colony algorithm for solving TSP problems.

Cites in Papers - |

Cites in Papers - IEEE (5)

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1.
Han Pan, Xiaoming You, Sheng Liu, "High-Frequency Path Mining-Based Reward and Punishment Mechanism for Multi-Colony Ant Colony Optimization", IEEE Access, vol.8, pp.155459-155476, 2020.
2.
Saurabh Adhau, Shubhangi Shinde, Taufiq Monghal, Varsha Hole, "GlobeTrotter: An Optimal Travel Sequence Generator", 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp.852-857, 2018.
3.
Jasdeep Kour, Sheela Tiwari, "Ant Colony Optimization - a tool for online tuning of a PI controller for a three phase induction motor drive", 2013 International Conference on Control, Automation, Robotics and Embedded Systems (CARE), pp.1-5, 2013.
4.
Mohammad Shokouhifar, Shima Sabet, "PMACO: A pheromone-mutation based ant colony optimization for traveling salesman problem", 2012 International Symposium on Innovations in Intelligent Systems and Applications, pp.1-5, 2012.
5.
Li-Pei Wong, Malcolm Yoke Hean Low, Chin Soon Chong, "An efficient Bee Colony Optimization algorithm for Traveling Salesman Problem using frequency-based pruning", 2009 7th IEEE International Conference on Industrial Informatics, pp.775-782, 2009.

Cites in Papers - Other Publishers (4)

1.
Emre ÇİNTAŞ, Barış ÖZYER, Sinan HANAY, "Ontology-based Instantaneous Route Suggestion of Enemy Warplanes with Unknown Mission Profile", Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol.24, no.5, pp.803, 2020.
2.
G.E. Anaya Fuentes, E.S. Hernández Gress, J.C. Seck Tuoh Mora, J. Medina Marín, "Solución al Problema de Secuenciación de Trabajos mediante el Problema del Agente Viajero", Revista Iberoamericana de Automática e Informática Industrial RIAI, vol.13, no.4, pp.430, 2016.
3.
Ghassan Saleh Al-Dharhani, Zulaiha Ali Othman, Azuraliza Abu Bakar, "A Graph-Based Ant Colony Optimization for Association Rule Mining", Arabian Journal for Science and Engineering, vol.39, no.6, pp.4651, 2014.
4.
Lucio Mauro Duarte, Luciana Foss, Flavio Rech Wagner, Tales Heimfarth, Distributed, Parallel and Biologically Inspired Systems, vol.329, pp.221, 2010.
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