Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/5824
Title: A hybrid genetic algorithm and inver over approach for the travelling salesman problem
Authors: Arshad, S
Yang, S
Keywords: Genetic algorithms;Search problems;Travelling salesman problems
Issue Date: 2010
Publisher: IEEE
Citation: IEEE Congress on Evolutionary Computation (CEC 2010): 252 - 259, 18-23 Jul 2010
Abstract: This paper proposes a two-phase hybrid approach for the travelling salesman problem (TSP). The first phase is based on a sequence based genetic algorithm (SBGA) with an embedded local search scheme. Within the SBGA, a memory is introduced to store good sequences (sub-tours) extracted from previous good solutions and the stored sequences are used to guide the generation of offspring via local search during the evolution of the population. Additionally, we also apply some techniques to adapt the key parameters based on whether the best individual of the population improves or not and maintain the diversity. After SBGA finishes, the hybrid approach enters the second phase, where the inver over (IO) operator, which is a state-of-the-art algorithm for the TSP, is used to further improve the solution quality of the population. Experiments are carried out to investigate the performance of the proposed hybrid approach in comparison with several relevant algorithms on a set of benchmark TSP instances. The experimental results show that the proposed hybrid approach is efficient in finding good quality solutions for the test TSPs.
Description: This article posted here with permission of the IEEE - Copyright @ 2010 IEEE
URI: http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=5586216&queryText%3DA+hybrid+genetic+algorithm+and+inver+over+approach+for+the+travelling+salesman+problem%26openedRefinements%3D*%26filter%3DAND%28NOT%284283010803%29%29%26searchField%3DSearch+All
http://bura.brunel.ac.uk/handle/2438/5824
DOI: http://dx.doi.org/10.1109/CEC.2010.5586216
ISBN: 978-1-4244-6909-3
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

Files in This Item:
File Description SizeFormat 
Fulltext.pdf371.45 kBAdobe PDFView/Open


Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.