An improved genetic algorithm for the extended capacitated arc routing problem

Mei, Y, Tang, K and Yao, X 2009, 'An improved genetic algorithm for the extended capacitated arc routing problem', in Proceedings of the IEEE Congress on Evolutionary Computation, United States, 18-21 May 2009, pp. 1699-1706.


Document type: Conference Paper
Collection: Conference Papers

Title An improved genetic algorithm for the extended capacitated arc routing problem
Author(s) Mei, Y
Tang, K
Yao, X
Year 2009
Conference name IEEE Congress on Evolutionary Computation
Conference location United States
Conference dates 18-21 May 2009
Proceedings title Proceedings of the IEEE Congress on Evolutionary Computation
Publisher Institute of Electrical and Electronics Engineers
Place of publication United States
Start page 1699
End page 1706
Total pages 8
Abstract Capacitated Arc Routing Problem (CARP) has attracted much interest because of its wide applications in the real world. Recently, a memetic algorithm proposed by Lacomme et al. (LMA) has been demonstrated to be a competitive approach to CARP. The crossover operation of LMA is carried out based on an implicit representation scheme, while it conducts local search on the basis of an explicit representation scheme. Hence, the search process of LMA involves frequent switch between the spaces defined by the two representation schemes. However, a good solution in one space is not necessarily good in the other. In this paper, we show that the local search process of LMA might be ineffective due to such reason, and suggest adopting a more careful way to coordinate the local search. As a result, two new local search methods are proposed, which resulted in two improved LMA (ILMA) algorithms. Experimental results on benchmark instances of CARP showed that the ILMA significantly outperformed LMA in terms of solution quality, and sometimes even in terms of computational time. Furthermore, ILMA improved the best known solutions for 8 problem instances out of the total 24 instances
Subjects Neural, Evolutionary and Fuzzy Computation
DOI - identifier 10.1109/CEC.2009.4983146
Copyright notice © 2009 IEEE
ISBN 9781424429585
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