Improved Memetic Algorithm Based on Route Distance Grouping for Multiobjective Large Scale Capacitated Arc Routing Problems

摘要

The capacitated arc routing problem (CARP) has attracted considerable attention from researchers due to its broad potential for social applications. This paper builds on, and develops beyond, the cooperative coevolutionary algorithm based on route distance grouping (RDG-MAENS), recently proposed by Mei Although Mei’s method has proved superior to previous algorithms, we discuss several remaining drawbacks and propose solutions to overcome them. First, although RDG is used in searching for potential better solutions, the solution generated from the decomposed problem at each generation is not the best one, and the best solution found so far is not used for solving the current generation. Second, to determine which sub-population the individual belongs to simply according to the distance can lead to an imbalance in the number of the individuals among different sub-populations and the allocation of resources. Third, the method of Mei was only used to solve single-objective CARP. To overcome the above issues, this paper proposes improving RDG-MAENS by updating the solutions immediately and applying them to solve the current solution through areas shared, and then according to the magnitude of the vector of the route direction, and a fast and simple allocation scheme is proposed to determine which decomposed problem the route belongs to. Finally, we combine the improved algorithm with an improved decomposition-based memetic algorithm to solve the multiobjective large scale CARP (LSCARP). Experimental results suggest that the proposed improved algorithm can achieve better results on both single-objective LSCARP and multiobjective LSCARP.