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. © 2009 IEEE.
|Title of host publication
|2009 IEEE Congress on Evolutionary Computation, CEC 2009
|Number of pages
|Published - May 2009